JPWO2020162425A1 - Analyst, analysis method, and program - Google Patents

Abstract

The analysis device (100) includes an image processing unit (102) that images the detection result of the vibration sensor provided in the production facility (10), and a discriminator (110) that targets the imaged data for machine learning processing. It is provided with a generation unit (104) for generating the above and an analysis unit (106) for performing a state analysis process of the production equipment (10) using the discriminator (110).

Description

The present invention relates to an analysis device, an analysis method, and a program, and more particularly to an analysis device, an analysis method, and a program for analyzing data of a sensor that monitors the state of equipment.

There is a method of condition monitoring using vibration and acoustic sensors for manufacturing quality control of production materials using mechanical equipment. For example, it is possible to avoid manufacturing loss by acquiring vibration data generated by the processing machine during processing of production materials and stopping processing of production materials when abnormal vibration is detected, and to change the operating status of production equipment to vibration. It is attracting attention as a technology for improving the production efficiency of the manufacturing industry by monitoring and finding the optimum operating conditions for improving the efficiency of maintenance and extending the life of equipment.

Patent Document 1 discloses a method in which a sensor is attached to a device to be monitored and the device is monitored based on the time-series data measured by the sensor.

Japanese Unexamined Patent Publication No. 2009-270843

As in the system described in Patent Document 1 described above, if an attempt is made to detect an abnormality in equipment based on data collected by a sensor, the amount of data to be analyzed may increase. It is also possible to build a normal / abnormal state model by machine learning such as deep learning, but this method only extracts the state that is presumed to be abnormal, and only takes actions such as stopping the operation. Not applicable.

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a technique for efficiently and accurately monitoring the state of production equipment.

In each aspect of the present invention, the following configurations are adopted in order to solve the above-mentioned problems.

The first aspect relates to an analyzer.
The first analysis device according to the first aspect is
Image processing means for imaging the detection results of vibration sensors installed in production equipment,
A generation means for generating a discriminator by using the imaged data as a target of machine learning processing,
It has an analysis means for performing a state analysis process of the production equipment using the discriminator.
The second analysis device according to the first aspect is
Judgment means for performing abnormality determination processing of the production equipment using the first discriminator based on the detection result of the vibration sensor provided in the production equipment, and
An image processing means for imaging the detection result that could not be discriminated as normal or abnormal by the first discriminator, and
A generation means for generating a second discriminator using the imaged data as a target of machine learning processing,
It has an analysis means for performing a state analysis process of the production equipment using the second discriminator.

The second aspect relates to an analysis method performed by at least one computer.
The first analysis method according to the second aspect is
The analyzer is
The detection result of the vibration sensor installed in the production equipment is visualized and
A discriminator is generated by using the imaged data as a target of machine learning processing.
It includes performing a state analysis process of the production equipment using the discriminator.
The second analysis method according to the second aspect is
The analyzer is
Based on the detection result of the vibration sensor installed in the production equipment, the abnormality determination process of the production equipment is performed using the first discriminator.
The detection result that could not be discriminated as normal or abnormal by the first discriminator was imaged and imaged.
A second discriminator is generated by using the imaged data as a target of machine learning processing.
It includes performing a state analysis process of the production equipment using the second discriminator.

As another aspect of the present invention, it may be a program that causes at least one computer to execute the method of the second aspect, or it is a recording medium that can be read by a computer that records such a program. You may. This recording medium includes a non-temporary tangible medium.
This computer program contains computer program code that causes the computer to perform its analysis method on an analyzer when executed by the computer.

It should be noted that any combination of the above components and the conversion of the expression of the present invention between methods, devices, systems, recording media, computer programs and the like are also effective as aspects of the present invention.

Further, the various components of the present invention do not necessarily have to be individually independent, and a plurality of components are formed as one member, and one component is formed of a plurality of members. It may be that a certain component is a part of another component, a part of a certain component overlaps with a part of another component, and the like.

Further, although the method and the computer program of the present invention describe a plurality of procedures in order, the order of description does not limit the order in which the plurality of procedures are executed. Therefore, when implementing the method and computer program of the present invention, the order of the plurality of procedures can be changed within a range that does not hinder the contents.

Furthermore, the methods of the present invention and the plurality of procedures of a computer program are not limited to being executed at different timings. Therefore, another procedure may occur during the execution of a certain procedure, a part or all of the execution timing of the certain procedure and the execution timing of the other procedure may overlap, and the like.

According to each of the above aspects, it is possible to provide a technique for efficiently and accurately monitoring the state of production equipment.

The above-mentioned objectives and other objectives, features and advantages are further clarified by the preferred embodiments described below and the accompanying drawings below.

It is a figure which conceptually shows the system configuration of the equipment monitoring system using the analysis apparatus which concerns on embodiment of this invention. It is a figure which shows an example of the data structure of the vibration data and equipment information stored in the storage device of this embodiment. It is a block diagram which illustrates the hardware composition of each apparatus of this embodiment. It is a functional block diagram which shows the logical structure of the analysis apparatus of this embodiment. Is a diagram showing time-series data of measurement data before imaging. It is a figure which shows the image data imaged by the image processing unit. It is a flowchart which shows an example of the operation of the analysis apparatus of this embodiment. It is a flowchart which shows an example of the detailed flow of the imaging process of step S101 of FIG. It is a functional block diagram which shows the logical structure of the analysis apparatus of this embodiment. It is a flowchart which shows an example of the operation of the analysis apparatus of this embodiment. It is a flowchart which shows an example of the procedure of abnormality determination processing in the analysis apparatus of this embodiment. It is a functional block diagram which shows the logical structure of the image processing part of the analysis apparatus of this embodiment. It is a flowchart which shows an example of the operation of the image processing part of the analysis apparatus of this embodiment. It is a functional block diagram which shows the logical structure of the analysis apparatus of this embodiment. It is a figure which shows an example of the data structure of the correction information 30. It is a flowchart which shows an example of the detailed flow of the discrimination process by an analysis part. It is a flowchart which shows an example of the update processing procedure of the discriminator using the data extracted in step S406 of FIG. It is a flowchart which shows an example of the processing procedure when it is determined to be normal in step S401 of the state analysis processing of FIG. It is a flowchart which shows an example of the procedure of the defect model construction process of an analysis apparatus. It is a flow diagram for demonstrating the analysis apparatus of Example 1. FIG. It is a flow diagram for demonstrating the analysis apparatus of Example 2.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In all drawings, similar components are designated by the same reference numerals, and the description thereof will be omitted as appropriate.

(First Embodiment)
FIG. 1 is a diagram conceptually showing a system configuration of an equipment monitoring system 1 using an analysis device according to an embodiment of the present invention.
The equipment to be monitored by the equipment monitoring system 1 is the production equipment 10, and in the present embodiment, a belt conveyor will be described as an example. In the example of the figure, a plurality of sensors 12 for monitoring the belt conveyor are installed at a plurality of locations along the moving direction of the belt conveyor. Each sensor 12 is, for example, a vibration sensor. Further, in the case of a vibration sensor that detects vibration in one direction, a plurality of vibration sensors may be installed at one location in order to detect vibration in a plurality of directions.

The measurement data output from the vibration sensor is time series data showing the vibration waveform.
The vibration sensor measures the vibration generated in the production equipment 10 to be monitored. The vibration sensor may be a uniaxial acceleration sensor that measures acceleration in the uniaxial direction, a triaxial accelerometer that measures acceleration in the triaxial direction, or another. The plurality of vibration sensors may be the same type of vibration sensor, or a plurality of types of vibration sensors may be mixed.

The analysis device 100 is connected to the GW (GateWay) 5 via the network 3 and receives detection results from a plurality of sensors 12 provided in the production equipment 10. The analysis device 100 is connected to the storage device 20. The storage device 20 stores vibration data analyzed by the analysis device 100. The storage device 20 may be a device separate from the analysis device 100, a device included inside the analysis device 100, or a combination thereof.

In each figure of the embodiment, the configuration of the portion not related to the essence of the present invention is omitted and is not shown.

FIG. 2 is a diagram showing an example of the data structure of the vibration data 22 and the equipment information 24 stored in the storage device 20 of the present embodiment.
In the vibration data 22, time information and measurement data are associated with each sensor ID that identifies the vibration sensor. In the equipment information 24, at least one sensor ID of a vibration sensor is associated with each equipment ID that identifies the equipment.

FIG. 3 is a block diagram illustrating a hardware configuration of each device of the present embodiment. Each device has a processor 50, a memory 52, an input / output interface (I / F) 54, a peripheral circuit 56, and a bus 58. The peripheral circuit 56 includes various modules. The processing device does not have to have the peripheral circuit 56.

The bus 58 is a data transmission path for the processor 50, the memory 52, the peripheral circuit 56, and the input / output interface 54 to transmit data to each other. The processor 50 is an arithmetic processing unit such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The memory 52 is, for example, a memory such as a RAM (Random Access Memory) or a ROM (Read Only Memory). The input / output interface 54 includes an interface for acquiring information from an input device, an external device, an external server, a sensor, and the like, an interface for outputting information to an output device, an external device, an external server, and the like. The input device is, for example, a keyboard, a mouse, a microphone, or the like. The output device is, for example, a display, a speaker, a printer, a mailer, or the like. The processor 50 can issue a command to each module and perform a calculation based on the calculation result thereof.

Each component of the analysis device 100 of the present embodiment shown in FIG. 4, which will be described later, is realized by any combination of the hardware and software of the computer shown in FIG. And, it is understood by those skilled in the art that there are various variations in the method of realizing the device and the device. The functional block diagram showing the analysis device of each embodiment described below shows a block of logical functional units, not a configuration of hardware units.

The processor 50 can realize each function of each unit of the analysis device 100 of FIG. 4 by reading the program into the memory 52 and executing the program.

In the computer program of the present embodiment, a procedure for imaging the detection result of the vibration sensor 12 provided in the production equipment 10 and the imaged data are input to the computer (processor 50 in FIG. 3) for realizing the analysis device 100. It is described so as to execute a procedure of generating a discriminator as a target of machine learning processing and a procedure of performing an abnormality determination process of the production equipment 10 using the discriminator.

The computer program of this embodiment may be recorded on a computer-readable recording medium. The recording medium is not particularly limited, and various forms can be considered. Further, the program may be loaded from the recording medium into the memory 52 of the computer (FIG. 3), or may be downloaded to the computer through the network and loaded into the memory 52.

A recording medium for recording a computer program includes a medium that can be used by a non-temporary tangible computer, and a computer-readable program code is embedded in the medium. When the computer program is executed on the computer, the computer is made to execute the analysis method of the present embodiment that realizes the analysis device 100.

FIG. 4 is a functional block diagram showing a logical configuration of the analysis device 100 of the present embodiment. The analysis device 100 includes an image processing unit 102, a generation unit 104, and an analysis unit 106.
The image processing unit 102 images the detection result of the vibration sensor provided in the production equipment 10. The generation unit 104 generates the discriminator 110 by targeting the imaged data to the machine learning process. The analysis unit 106 performs a state analysis process of the production equipment 10 by using the discriminator 110.

Vibration consists of three components: amplitude, frequency, and phase, and measurement data can be presented with three parameters: displacement, velocity, and acceleration. Since the measurement data of the vibration sensor is time-series data including a plurality of vibration waveforms indicated by a plurality of parameters, the characteristics cannot be captured even if the machine learning is applied as it is, and an appropriate learning model cannot be generated. Therefore, in the present embodiment, the measurement data is imaged by the image processing unit 102. FIG. 5 is a diagram showing time-series data of measurement data before imaging. FIG. 6 is a diagram showing image data imaged by the image processing unit 102.

The image processing unit 102 acquires the detection result (hereinafter, also referred to as vibration data) of the sensor 12 of the production equipment 10. Then, the image processing unit 102 performs FFT processing on the acquired vibration data, divides the frequency, and images the obtained vibration spectrum data to obtain image data. By these processes, the data capacity can be compressed, and the machine learning process and the discrimination process can be speeded up.

In the embodiment, "acquisition" means that the own device retrieves data or information stored in another device or storage medium (active acquisition), and is output to the own device from the other device. Includes at least one of entering data or information (passive acquisition). Examples of active acquisition include making a request or inquiry to another device and receiving the reply, and accessing and reading another device or storage medium. Further, an example of passive acquisition may be receiving information to be delivered (or transmitted, push notification, etc.). Further, "acquisition" may be to select and acquire from received data or information, or to select and receive delivered data or information.

The discriminator 110 is generated by the generation unit 104 with the imaged data as the target of machine learning processing. The analysis unit 106 uses this discriminator 110 to perform a state analysis process of the production equipment 10.

Machine learning can use, for example, deep learning, but is not limited to this. The state of the production equipment 10 determined by the discriminator 110 is, for example, normal or other. Hereinafter, the state other than normal is also called "Unknown". In the present embodiment, the discriminator 110 does not discriminate the abnormal state. The machine learning process of the discriminator 110 will be described in detail in the embodiment described later.

The discrimination result of the discriminator 110 may be output so that the operator can refer to it. The output method may be displayed on the display of the analysis device 100, may be printed out from the analysis device 100 to a printer, or may be transmitted to another device (for example, an operation terminal or the like) via a communication line. May be done.

The operation of the analysis device 100 of the present embodiment configured in this way will be described. FIG. 7 is a flowchart showing an example of the operation of the analysis device 100 of the present embodiment.
First, the image processing unit 102 images the detection result of the vibration sensor provided in the production equipment 10 (step S101). Then, the generation unit 104 generates the discriminator 110 by targeting the imaged data to the machine learning process (step S103). Then, the analysis unit 106 performs a state analysis process of the production equipment 10 using the discriminator 110 (step S105).

FIG. 8 is a flowchart showing an example of the detailed flow of the imaging process in step S101 of FIG. 7. First, the image processing unit 102 performs FFT processing on the measurement data of the sensor 12, then frequency-divides the measurement data (step S113), images the obtained vibration spectrum data, and outputs the image data (step S115). The image data obtained in step S115 is subjected to state analysis processing of the production equipment 10 using the discriminator 110 by the analysis unit 106 in step S103 of FIG.

As described above, in the present embodiment, after the measurement data of the sensor 12 is imaged by the image processing unit 102, the image data imaged by the generation unit 104 is targeted for machine learning processing by the discriminator 110. It is generated, and the state analysis process of the production equipment 10 is performed by the analysis unit 106 using the discriminator 110. As described above, according to the present embodiment, when the detection result of the vibration sensor is machine-learned, the data amount can be compressed by imaging the vibration waveform data to be the target of the machine learning process, so that the processing load can be reduced. It can be reduced and the processing speed can be increased.

(Second embodiment)
FIG. 9 is a functional block diagram showing a logical configuration of the analysis device 100 of the present embodiment. The analysis device 100 includes an image processing unit 102, a generation unit 104, and an analysis unit 106 similar to the analysis device 100 of FIG. 4, and further includes an abnormality determination unit 120.

The abnormality determination unit 120 performs the abnormality determination process of the production equipment 10 based on the detection result of the vibration sensor provided in the production equipment 10 by using the first discriminator 124. The abnormality determination unit 120 outputs the result of the abnormality determination process using the first discriminator 124. The output result may be used for the monitoring process of the production equipment 10 in the equipment monitoring system 1.

Further, the image processing unit 102 of the present embodiment is the image processing of the above embodiment in that the detection result to be imaged is a detection result in which it cannot be determined whether the image is normal or abnormal by the first discriminator 124. It is different from the part 102. The generation unit 104 generates a second discriminator 126 for machine learning processing of the measurement data whose normality or abnormality could not be discriminated by the threshold value analysis in this way.

The vibration measured by the vibration sensor includes a plurality of vibration waveforms composed of a plurality of factors. Generally, the measurement data detected from the vibration sensor is subjected to frequency analysis by performing a fast Fourier transform (FFT) process. By performing FFT processing on the measured data, a characteristic frequency (peak) is detected, and it is possible to diagnose an abnormality by determining whether the detected peak level is normal or abnormal by a threshold value.

However, it may not be possible to determine whether it is normal or abnormal by this threshold analysis. The generation unit 104 generates the second discriminator 126 by using the measurement data that could not be discriminated as normal or abnormal by the threshold value analysis as the target of the machine learning process. The second discriminator 126 corresponds to the discriminator 110 in FIG.

The case where "the first discriminator 124 cannot discriminate" means that, for example, when the vibration waveform pattern registered in the first discriminant model 128 and the detection result do not match, the likelihood of the matching result is equal to or higher than a predetermined value. For example, when the reliability is not obtained and the reliability is higher than the standard required for equipment diagnosis (for example, the detection rate is 90% or higher).

The analysis unit 106 performs a state analysis process of the production equipment 10 by using the second discriminator 126. The analysis unit 106 performs a state analysis process of the production equipment 10 by using the second discriminator 126 for the data obtained by imaging the detection result that could not be discriminated as normal or abnormal by the first discriminator 124.

The first discriminator 124 discriminates whether it is normal or abnormal by using the first discriminant model 128. The first discrimination model 128 is a model using, for example, pattern matching of vibration spectra and threshold value determination.

As an example, the first discriminator 124 uses the frequency distribution obtained by FFT processing the measurement data of the vibration sensor to obtain the peak level of a specific frequency, the ratio of the maximum and average peak levels, and the signal-to-noise ratio (S / N ratio). -Noise ratio) and at least one of the integrated values of the peak level in a specific frequency range are obtained, a threshold is set for the obtained value, and abnormality judgment processing is performed based on whether or not it is within the threshold range. conduct. If it is within the threshold range, it is determined to be normal, and if it is outside the threshold range, it is determined to be abnormal.

The first discriminator 124 may perform the above-mentioned determination process using a plurality of specific frequencies. In that case, the first discriminator 124 may output the determination result for each specific frequency. Further, the first discriminator 124 may determine that an abnormality occurs when even one of the plurality of specific frequencies has a value that deviates from the threshold value, or specifies a predetermined number or more of the plurality of specific frequencies. If the frequency has a value outside the threshold value, it may be determined to be abnormal, if all of a plurality of specific frequencies are out of the threshold value, it may be determined to be abnormal, or all of the plurality of specific frequencies. May be determined to be normal when is within the threshold range. Further, a combination of a plurality of specific frequency values is modeled for each malfunction event and registered in the first discrimination model 128, and the first discriminator 124 fails due to pattern matching with the first discrimination model 128. The event may be discriminated. The abnormality determination unit 120 may notify the operator of the determined defect event item.

The threshold value may be set automatically or may be set manually by the operator. In the case of automatic setting, the generation unit 104 has the peak level of a specific frequency, the ratio of the maximum and average peak levels, and the S / N ratio (for the vibration characteristic pattern (frequency distribution) registered in the first discrimination model 128). The signal-to-noise ratio) and at least one of the integrated values of the peak levels in a specific frequency range may be obtained, and the boundary range between normal and abnormal times may be detected and a threshold value may be set.

In the case of manual setting, the generation unit 104 regarding the vibration characteristic pattern (frequency distribution) registered in the first discrimination model 128, the peak level of a specific frequency, the ratio of the maximum and average peak levels, and the S / N ratio (Signal). -to-Noise ratio) and the result obtained for at least one of the integrated values of the peak levels in a specific frequency range are presented to the operator, and the threshold value of the first discriminator 124 is set by the operator. May be accepted respectively. There are various ways to present the result to the operator, but as an example, it may be displayed on the display of the analysis device 100, may be printed out from the analysis device 100 to the printer, or may be printed out by another communication line. It may be transmitted to a device (for example, an operation terminal or the like).

The second discriminator 126 is generated by the generation unit 104 and corresponds to the discriminator 110 generated by the generation unit 104 of the above embodiment. The generation unit 104 machine-learns only the data determined to be normal by the second discriminator 126, normalizes the normal state, and updates the second discriminant model 130. Here, normalization is to model the pattern of the imaged data of the measurement data of the vibration sensor at the normal time. The second discriminator 126 extracts data outside the range of the second discriminant model 130, for example, as “Unknown”. That is, with respect to the detection result that could not be discriminated by the first discriminator 124 by the analysis unit 106 using the second discriminator 126, the state of the production equipment 10 is further determined based on the second discriminant model 130. , Normal or "Unknown" is determined. The second discriminator 126 does not discriminate the abnormal state of the production equipment 10, and discriminates that the non-normal state is "Unknown".

The data determined to be "Unknown" by the discriminator 110 may be referred to and analyzed by the operator. The result analyzed by the operator may be reflected in the threshold value of the abnormality determination processing of the detection result. The method of using the data determined to be "Unknown" will be described in detail in the embodiment described later.

The operation of the analysis device 100 of the present embodiment configured in this way will be described. FIG. 10 is a flowchart showing an example of the operation of the analysis device 100 of the present embodiment.
First, the abnormality determination unit 120 performs the abnormality determination process of the production equipment 10 based on the detection result of the vibration sensor provided in the production equipment 10 by using the first discriminator 124 (step S121). Then, the image processing unit 102 images the detection result (NO in step S123) for which the first discriminator 124 cannot determine whether it is normal or abnormal (step S125). If it can be determined (YES in step S123), this process is terminated.

Then, the generation unit 104 generates the second discriminator 126 by targeting the imaged data to the machine learning process (step S127). Then, the analysis unit 106 performs a state analysis process of the production equipment 10 using the second discriminator 126 (step S129).

Further, in the computer program of the present embodiment, the computer (processor 50 in FIG. 3) for realizing the analysis device 100 has an abnormality in the production equipment 10 based on the detection result of the vibration sensor 12 provided in the production equipment 10. The procedure for performing the determination process using the first discriminator 124, the procedure for imaging the detection result that could not be discriminated as normal or abnormal by the first discriminator 124, and the object of the machine learning process for the imaged data. The procedure for generating the second discriminator 126 and the procedure for performing the state analysis process of the production equipment 10 using the second discriminator 126 are described as being executed.

As described above, in the present embodiment, when the abnormality determination unit 120 does not obtain the discrimination result in the abnormality determination process based on the detection result of the vibration sensor using the first discriminator 124, the vibration at that time. The detection result of the sensor is imaged by the image processing unit 102, and whether it is normal or "Unknown" is determined by the second discriminator 126. As described above, according to the present embodiment, the second discriminator 126 is machine-learned only for the detection result of the vibration sensor corresponding to the normal state, and normalizes the normal state. Even in the case of production where the manufacturing conditions are always fluid but it is difficult to accumulate information on failure events, the accuracy of discrimination can be improved.

Further, as in the above embodiment, when the detection result of the vibration sensor is machine-learned, the data amount can be compressed by imaging the vibration waveform data to be the target of the machine learning process, so that the processing load is reduced. At the same time, the processing speed can be increased.

(Third embodiment)
FIG. 11 is a flowchart showing an example of the procedure of abnormality determination processing in the analysis device 100 of the present embodiment. This embodiment is the same as the analysis device 100 of FIG. 9 except that it has a configuration for outputting the result of the abnormality determination process of the production equipment 10 using the first discriminator 124.

First, the abnormality determination unit 120 acquires vibration data from the vibration sensor provided in the production equipment 10 (step S301). Then, the abnormality determination unit 120 executes the abnormality determination process of the vibration data acquired by using the first discriminator 124 (step S303). Here, after the vibration data is FFT-processed, normality or abnormality is determined by the first determination model 128 of the abnormality determination unit 120 (step S305).

From the frequency distribution obtained by FFT processing, the abnormality determination unit 120 has a peak level of a specific frequency, a ratio of the maximum and average peak levels, an S / N ratio (Signal-to-Noise ratio), and a range of the specific frequency. At least one of the integrated values of the peak levels of the above is obtained, and it is determined whether these values are normal or abnormal by using the threshold value (step S305). When it is within the threshold range (NO in step S305), the first discriminator 124 determines that the production equipment 10 is normal (step S307), and the abnormality determination unit 120 determines that the production equipment 10 is normal. Is output (step S311).

When it is out of the threshold range (YES in step S305), the first discriminator 124 determines that the production equipment 10 is in an abnormal state (step S309), and the determination result indicating that the production equipment 10 is in an abnormal state. Is output (step S311).

Further, when the first discriminator 124 cannot discriminate between normal and abnormal (discrimination in step S305 is not possible), the abnormality determining unit 120 outputs vibration data to the image processing unit 102 (step S313). The process proceeds to step S125 of 10.

In the present embodiment, the first discriminator 124 is used for the abnormality determination process of the production equipment 10, and the second discriminator 126 is not used for the abnormality determination process. Further, the discrimination result of the second discriminator 126 is used to update the second discriminant model 130 of the first discriminator 124, and this configuration will be described in the embodiment described later.

As described above, in the present embodiment, the abnormality determination unit 120 performs the abnormality determination process of the production equipment 10 using the first discriminator 124, and the abnormality determination unit 120 uses the second discriminator 126 as the production equipment 10. It is not used for the abnormality judgment processing of. The first discriminator 124 can be updated by using the discriminant result of the second discriminator 126 updated by the generation unit 104. According to this configuration, the abnormality determination process can be performed using the first discriminator 124 that reflects the discriminant result of the second discriminator 126, so that the accuracy of the determination result can be improved.

(Fourth Embodiment)
FIG. 12 is a functional block diagram showing a logical configuration of the image processing unit 102 of the analysis device 100 of the present embodiment. The analysis device 100 of this embodiment is the same as that of the above embodiment except that the image processing unit 102 has a configuration in which noise is removed from the measurement data and then imaging processing is performed. The configuration of this embodiment may be combined with the configuration of any other embodiment.

The image processing unit 102 includes a noise removing unit 112 and an image processing unit 114. The noise removing unit 112 performs noise removing processing on the detection result. The image processing unit 114 images the detection result after the noise removal processing by the noise removal unit 112 is performed. Noise removal makes the vibration waveform of the measurement data clear.

The noise removal processing includes, for example, measuring and storing environmental noise from a plurality of arranged vibration sensors in advance, and performing differential processing based on the noise data. However, the noise removal process may be performed by another method.

FIG. 13 is a flowchart showing an example of the operation of the image processing unit 102 of the analysis device 100 of the present embodiment. The flowchart of FIG. 13 further includes step S111 in addition to steps S113 and S115 of the flowchart of FIG. 8 described in the above embodiment.

In step S111, the noise removing unit 112 performs noise removing processing of the measurement data. Then, the data that has been noise-removed in step S111 is subjected to FFT processing by the image processing unit 114 and then frequency division processing (step S113), and the obtained vibration spectrum data is imaged and output as image data (step S115). ). The image data obtained in step S115 is subjected to state analysis processing of the production equipment 10 using the discriminator 110 by the analysis unit 106 in step S103 of FIG.

As described above, in the present embodiment, the measurement data of the sensor 12 that has been noise-removed by the noise-removing unit 112 is imaged by the imaging processing unit 114. With this configuration, according to the present embodiment, it is possible to speed up the machine learning process or the discrimination process as in the above embodiment, and further, the vibration waveform of the measurement data becomes clear by the noise removal, and the FFT process and the FFT process are performed. The accuracy of the imaging process is improved, and the accuracy and reliability of the analysis result of the measurement data are improved.

(Fifth Embodiment)
FIG. 14 is a functional block diagram showing a logical configuration of the analysis device 100 of the present embodiment. The analysis device 100 of the present embodiment extracts data determined to be abnormal by the state analysis process of the production equipment 10 from the above embodiment, and based on the data, the first first discriminator 124 of the first discriminator 124. It is the same as the above-described embodiment except that it has a configuration for updating the discrimination model 128.

The analysis device 100 includes an image processing unit 102 similar to the analysis device 100 of FIG. 9, a generation unit 104, an analysis unit 106, an abnormality determination unit 120, and further includes an extraction unit 140.

The extraction unit 140 extracts data determined to be abnormal (“Unknown”) in the state analysis process using the second discriminator 126. The generation unit 104 receives the correction information based on the extracted data and updates the first discrimination model 128 of the first discriminator 124. Here, the correction information includes information linking the malfunction event and the vibration characteristic.

The extracted data is the raw vibration waveform data received from the vibration sensor before the image processing unit 102 performs the image processing, which corresponds to the imaged data determined to be "Unknown", and the time information of the data. including. The vibration indicated by the vibration data determined to be "Unknown" may be caused by a malfunction event of the production equipment 10 which has not been specified yet.

Therefore, the operator manually analyzes the extracted vibration data together with the time information, using the operation information, state information, work information, etc. of the production equipment 10 possessed by the equipment monitoring system 1, and the vibration. Identify the malfunctioning event that caused the problem.

Therefore, the analysis device 100 outputs the data extracted by the extraction unit 140 and presents it to the operator. Various methods of presenting the data to the operator can be considered, but as an example, the data may be displayed on the display of the analysis device 100, may be printed out from the analysis device 100 to the printer, or may be printed out by another communication line. It may be transmitted to a device (for example, an operation terminal or the like).

The operator inputs the correction information 30 of FIG. 15 in which the vibration characteristic information of the corresponding vibration is associated with the defect information specified by the operator to the analysis device 100 using an operation screen or the like. The generation unit 104 receives the input correction information 30, and updates the first discrimination model 128 of the first discriminator 124.

FIG. 16 is a flowchart showing an example of the detailed flow of the discrimination process by the analysis unit 106. The analysis unit 106 applies the image data output in step S115 of FIG. 13 to the second discriminator 126 to perform a state analysis process of the production equipment 10 (step S401). The data determined to be normal by the second discriminator 126 (“normal” in step S401) is passed to the generation unit 104 and machine-learned as normal data (step S403). On the other hand, the analysis unit 106 extracts data out of the normalization range (“Unknown” in step S401) by the second discriminator 126 (step S405).

In addition, using the flow of FIG. 16, the image data output in step S115 of FIG. 8 may be processed in the same manner.

FIG. 17 is a flowchart showing an example of an update processing procedure of the discriminator using the data extracted in step S405 of FIG.
First, the extraction unit 140 outputs the data (vibration data and time information) extracted in step S405 (step S411). Here, for example, it is displayed on the display of the analysis device 100. Then, the operator analyzes the vibration data displayed in step S411 together with the time information by using the operation information, the state information, the processed product information, and the like of the production equipment 10 possessed by the equipment monitoring system 1. Identify the malfunction event that caused the vibration. The operator creates information in which the specified malfunction event and the corresponding vibration characteristic information are associated with each other, and inputs the information as the correction information of the threshold value of the first discriminator 124 according to the operation screen of the analysis device 100.

The generation unit 104 receives the input correction information (step S413), and updates the first discriminator 124 using the received correction information (step S415).

Specifically, the vibration characteristic information and the malfunction event included in the received correction information are registered in the first discrimination model 128, and the peak level of a specific frequency, the ratio of the maximum and average peak levels, and the S / N ratio ( Signal-to-Noise ratio) and at least one of the integrated values of peak levels in a specific frequency range are obtained, and the boundary range between normal and abnormal is detected and a threshold is set. Alternatively, the operator may set a threshold value based on each value obtained from the vibration characteristic information corresponding to the specified malfunction event and input the threshold value using the operation screen.

As described above, in the present embodiment, the "Unknown" data of the second discriminator 126 is extracted by the extraction unit 140, presented to the operator, and the malfunction event and vibration characteristic information analyzed by the operator by the generation unit 104. The correction information associated with is received, and the first discriminator 124 is updated based on the correction information. As described above, according to the present embodiment, the measurement data that could not be discriminated by the first discriminator 124 is further discriminated by the second discriminator 126, and the data designated as "Unknown" is extracted. Since the operator analyzes and reflects the result in the first discriminator 124, the accuracy of the abnormality determination process can be improved.

In addition, since the second discriminator 126 is machine-learned only for normal information, it may be difficult to accumulate information on defective events even though the manufacturing conditions are always fluid due to small-lot, high-mix production, variable production, and the like. However, since the first discrimination model 128 can be updated, the determination accuracy of the abnormal state of the production equipment 10 can be improved.

(Variation of the fifth embodiment)
The analysis device 100 of FIG. 14 has a configuration in which an extraction unit 140 is provided in the configuration of the analysis device 100 of FIG. As a modification thereof, the analysis device 100 of FIG. 4 may be provided with an extraction unit 140.

The analysis device 100 further includes an abnormality determination unit 120 and an extraction unit 140. The abnormality determination unit 120 performs a characteristic analysis process of the vibration of the vibration sensor indicated by the detection result, and performs an abnormality determination process of the production equipment 10 using the threshold value. The extraction unit 140 extracts data determined by the state analysis process that the production equipment 10 is not in a normal state. The generation unit 104 receives the correction information based on the extracted data and updates the threshold value. The correction information includes information linking the malfunction event and the vibration characteristic.

Although the embodiments of the present invention have been described above with reference to the drawings, these are examples of the present invention, and various configurations other than the above can be adopted.
For example, FIG. 18 is a flowchart showing an example of a processing procedure when it is determined to be normal in step S401 of the state analysis processing of FIG. This embodiment is the same as the above embodiment except that it has a configuration in which the detection result determined to be normal by the second discriminator 126 is used as the teacher data of the second discriminator 126.

The generation unit 104 uses the detection result determined to be normal by the state analysis process using the second discriminator 126 (normality in step S401 in FIG. 16) as the second teacher data of the normal state of the production equipment 10. The second discrimination model 130 of the discriminator 126 is updated (step S501).

According to this configuration, the second discrimination model 130 is updated by the generation unit 104 as the teacher data of the normal state of the production equipment 10 from the detection result determined to be normal by the state analysis process using the second discriminator 126. Will be done. In this way, the teacher data in the normal state can be generated from the measurement data that could not be discriminated in the abnormality determination process by the first discriminator 124, and the second discriminant model 130 can be updated. It is possible to improve the discrimination accuracy of.

Further, in addition to the first discriminator 124 and the second discriminator 126 of the above embodiment, the analysis device 100 may include a third discriminator (not shown). FIG. 19 is a flowchart showing an example of a processing procedure in which the analysis device 100 constructs a defect model using a third discriminator and identifies a defect event. The third discriminator acquires the measurement data determined to be normal by the first discriminator 124 (step S601). Further, the third discriminator acquires the measurement data discriminated as Unknown by the second discriminator 126 (step S603). Then, the third discriminator machine-learns these data and constructs a model that classifies the failure events (step S605). The noise removal processing and the imaging processing described in the above embodiment may also be performed on each measurement data machine-learned by the third discriminator.

Further, using the third discriminator, it is determined whether the measurement data of the sensor 12 of the production equipment 10 is normal or abnormal, and further, in the case of abnormality, a malfunction event is identified (step S607).

Further, the position information of each of the plurality of sensors 12 is stored in the equipment information 24, the relationship between the vibration data and the position information is further machine-learned, and the classification model 202, the first discrimination model 128, and the second discrimination model 128 are further machine-learned. It may be reflected in at least one of the discrimination models 130.

Further, information such as measurement conditions (equipment type, environment (temperature, humidity), etc.) of the measurement data of the plurality of sensors 12 may be stored in the equipment information 24. The measurement data under the conditions close to the measurement conditions, the operation state included in the operation information of the production equipment 10, the operation conditions, etc. are grouped, and the measurement data is machine-learned for each group, and the classification model 202 and the first discrimination model 128 are used. , And at least one of the second discrimination model 130.

(Example 1)
FIG. 20 is a flow chart for explaining the analysis device of the first embodiment.
First, when the vibration data is input from the sensor 12 of the production equipment 10, the abnormality determination unit 120 performs the FFT process in the first classifier 124 (step S11). At this time, the vibration characteristics are specified by pattern matching processing using the first discrimination model 128. Then, the first discriminator 124 determines that the vibration characteristic is normal when it is within the range of the threshold value, and determines that it is abnormal when it is out of the range of the threshold value 210. The abnormality determination unit 120 outputs this result to the equipment monitoring system 1 as an abnormality determination result of the production equipment 10 (not shown).

Further, the abnormality determination unit 120 extracts data for which normality or non-determination could not be determined by the first discriminator 124 in step S11 (step S13), and targets the machine learning process of the second discriminator 126. Hand over to the analysis unit 106.

The analysis unit 106 performs noise removal processing on the measurement data of the sensor 12 that could not be discriminated by the first discriminator 124 (step S15), frequency-analyzes and images (step S17). The analysis unit 106 discriminates the imaged data by using the second discriminator 126 (step S19), and discriminates whether or not the state of the production equipment 10 is normal (step S21). When it is determined to be normal (YES in step S21), the generation unit 104 machine-learns the measurement data determined to be normal and updates the second discrimination model 130 of the second discriminator 126 (step S23). ..

On the other hand, when it is not determined to be normal (“Unknown”) (NO in step S21), the extraction unit 140 extracts and outputs the measurement data that has become unknown (step S31). The operator refers to the extracted unknown data and analyzes it together with the operation information of the production equipment 10 to identify the malfunction event. Then, the correction information associated with the malfunction event and the vibration characteristic is input to the analysis device 100 (step S33).

The generation unit 104 receives the input correction information, and updates the first discrimination model 128 and the threshold value based on the received correction information (step S35).

In this way, the analysis device 100 machine-learns the measurement data for which the abnormality could not be determined by the first discriminator 124 by the second discriminator 126, thereby extracting the abnormal unknown data and producing the production equipment. The operator identifies the malfunction event by analyzing it together with the operation information of 10, and inputs it to the analysis device 100 as the correction information linking the vibration characteristic and the malfunction event. The first discriminant model 128 and the threshold can be updated.

(Example 2)
FIG. 21 is a flow chart for explaining the analysis device of the second embodiment.
The analysis device 100 of this embodiment includes a first discriminator 124, a second discriminator 126, and a third discriminator 200.
The third discriminator 200 performs machine learning using the measurement data determined to be normal by the first discriminator 124 and the measurement data determined to be unknown by the second discriminator 126 (step S41). The second discriminator 126 constructs a normal and abnormal classification model 202 by machine learning. The classification model 202 classifies failure events.

The third discriminator 200 can discriminate whether the measurement data is normal or abnormal by using the classification model 202, and can further discriminate and identify the malfunction event.

Although the present invention has been described above with reference to the embodiments and examples, the present invention is not limited to the above embodiments and examples. Various changes that can be understood by those skilled in the art can be made within the scope of the present invention in terms of the configuration and details of the present invention.
In addition, when information about a user is acquired and used in this invention, this shall be done legally.

Some or all of the above embodiments may also be described, but not limited to:
1. 1. Image processing means for imaging the detection results of vibration sensors installed in production equipment,
A generation means for generating a discriminator by using the imaged data as a target of machine learning processing,
An analysis device including an analysis means for performing a state analysis process of the production equipment using the discriminator.
2. 2. 1. 1. In the analyzer described in
A determination means for performing characteristic analysis processing of the vibration of the vibration sensor indicated by the detection result and performing abnormality determination processing for the production equipment using a threshold value.
Further provided with an extraction means for extracting data determined by the state analysis process that the production equipment is not in a normal state.
The generation means receives the correction information based on the extracted data, updates the threshold value, and receives the correction information.
The correction information is an analysis device including information linking a malfunction event and a vibration characteristic.
3. 3. Judgment means for performing abnormality determination processing of the production equipment using the first discriminator based on the detection result of the vibration sensor provided in the production equipment, and
An image processing means for imaging the detection result that could not be discriminated as normal or abnormal by the first discriminator, and
A generation means for generating a second discriminator using the imaged data as a target of machine learning processing,
An analysis means for performing a state analysis process of the production equipment using the second discriminator, and an analysis means.
To prepare
Analytical device.
4. 3. 3. In the analyzer described in
Further provided with an extraction means for extracting data determined by the state analysis process that the production equipment is not in a normal state.
The generation means receives the correction information based on the extracted data, updates the first discriminator, and updates the first discriminator.
The correction information is an analysis device including information linking a malfunction event and a vibration characteristic.
5. 1. 1. From 4. In the analyzer described in any one of
The generation means is an analysis device that uses the data determined to be normal by the state analysis process as teacher data for the machine learning process.
6. 1. 1. From 5. In the analyzer described in any one of
Further provided with a processing means for performing noise removal processing on the detection result,
The image processing means is an analysis device that images the detection result after the noise removal processing by the processing means is performed.
7. 1. 1. From 6. In the analyzer described in any one of
The production equipment is a belt conveyor.
The vibration sensor is an analysis device which is a plurality of vibration sensors provided on the belt conveyor.

8. The analyzer is
The detection result of the vibration sensor installed in the production equipment is visualized and
A discriminator is generated by using the imaged data as a target of machine learning processing.
The state analysis process of the production equipment is performed using the discriminator.
analysis method.
9. 8. In the analysis method described in
The analysis device further
The vibration characteristic analysis process of the vibration sensor indicated by the detection result is performed, and the abnormality determination process of the production equipment is performed using the threshold value.
Data determined by the state analysis process that the production equipment is not in a normal state is extracted, and the data is extracted.
The correction information based on the extracted data is received, the threshold value is updated, and the threshold value is updated.
The correction information is an analysis method including information linking a malfunction event and a vibration characteristic.
10. The analyzer is
Based on the detection result of the vibration sensor installed in the production equipment, the abnormality determination process of the production equipment is performed using the first discriminator.
The detection result that could not be discriminated as normal or abnormal by the first discriminator was imaged and imaged.
A second discriminator is generated by using the imaged data as a target of machine learning processing.
The state analysis process of the production equipment is performed using the second discriminator.
analysis method.
11. 10. In the analysis method described in
The analysis device further
Data determined by the state analysis process that the production equipment is not in a normal state is extracted, and the data is extracted.
The correction information based on the extracted data is received, and the first discriminator is updated.
The correction information is an analysis method including information linking a malfunction event and a vibration characteristic.
12. 8. From 11. In the analysis method described in any one of
The analysis device further
An analysis method in which the data determined to be normal by the state analysis process is used as the teacher data of the machine learning process.
13. 8. From 12. In the analysis method described in any one of
The analysis device further
Noise removal processing is performed on the detection result, and the noise is removed.
An analysis method for imaging the detection result after the noise removal process is performed.
14. 8. From 13. In the analysis method described in any one of
The production equipment is a belt conveyor.
The analysis method, wherein the vibration sensor is a plurality of vibration sensors provided on the belt conveyor.

15. On the computer
Procedure for imaging the detection result of the vibration sensor installed in the production equipment,
A procedure for generating a discriminator by targeting the imaged data to machine learning processing,
A program for executing a procedure for performing an abnormality determination process of the production equipment using the discriminator.
16. 15. In the program described in
A procedure for performing a characteristic analysis process of vibration of the vibration sensor indicated by the detection result and performing an abnormality determination process of the production equipment using a threshold value.
A procedure for extracting data determined by the state analysis process that the production equipment is not in a normal state.
In the procedure for generating, the computer is further executed to receive the correction information based on the extracted data and update the threshold value.
The correction information is a program including information linking a malfunction event and a vibration characteristic.
17. On the computer
A procedure for performing abnormality determination processing of the production equipment using the first discriminator based on the detection result of the vibration sensor provided in the production equipment.
A procedure for imaging the detection result for which normality or abnormality could not be determined by the first discriminator.
A procedure for generating a second discriminator using the imaged data as a target of machine learning processing,
A program for executing a procedure for performing a state analysis process of the production equipment using the second discriminator.
18. 17. In the program described in
A procedure for extracting data determined by the state analysis process that the production equipment is not in a normal state.
In the procedure for generating, the computer is further made to execute the procedure for receiving the correction information based on the extracted data and updating the first discriminator.
The correction information is a program including information linking a malfunction event and a vibration characteristic.
19. 15. From 18. In the program described in any one of
A program for causing a computer to further perform a procedure of using the data determined to be normal by the state analysis process as teacher data of the machine learning process in the generated procedure.
20. 15. From 19. In the program described in any one of
Procedure for performing noise removal processing on the detection result,
A program for causing a computer to further perform a procedure for imaging the detection result after the noise removal process is performed in the imaging procedure.
21. 15. From 20. In the program described in any one of
The production equipment is a belt conveyor.
The vibration sensor is a program, which is a plurality of vibration sensors provided on the belt conveyor.

This application claims priority on the basis of Japanese Application Japanese Patent Application No. 2019-019068 filed on February 5, 2019 and incorporates all of its disclosures herein.

It is a figure which conceptually shows the system configuration of the equipment monitoring system using the analysis apparatus which concerns on embodiment of this invention. It is a figure which shows an example of the data structure of the vibration data and equipment information stored in the storage device of this embodiment. It is a block diagram which illustrates the hardware composition of each apparatus of this embodiment. It is a functional block diagram which shows the logical structure of the analysis apparatus of this embodiment. Is a diagram showing time-series data of measurement data before imaging. It is a figure which shows the image data imaged by the image processing unit. It is a flowchart which shows an example of the operation of the analysis apparatus of this embodiment. It is a flowchart which shows an example of the detailed flow of the imaging process of step S101 of FIG. It is a functional block diagram which shows the logical structure of the analysis apparatus of this embodiment. It is a flowchart which shows an example of the operation of the analysis apparatus of this embodiment. It is a flowchart which shows an example of the procedure of abnormality determination processing in the analysis apparatus of this embodiment. It is a functional block diagram which shows the logical structure of the image processing part of the analysis apparatus of this embodiment. It is a flowchart which shows an example of the operation of the image processing part of the analysis apparatus of this embodiment. It is a functional block diagram which shows the logical structure of the analysis apparatus of this embodiment. It is a figure which shows an example of the data structure of the correction information 30. It is a flowchart which shows an example of the detailed flow of the discrimination process by an analysis part. It is a flowchart which shows an example of the update processing procedure of the discriminator using the data extracted in step S405 of FIG. It is a flowchart which shows an example of the processing procedure when it is determined to be normal in step S401 of the state analysis processing of FIG. It is a flowchart which shows an example of the procedure of the defect model construction process of an analysis apparatus. It is a flow diagram for demonstrating the analysis apparatus of Example 1. FIG. It is a flow diagram for demonstrating the analysis apparatus of Example 2.

The bus 58 is a data transmission path for the processor 50, the memory 52, the peripheral circuit 56, and the input / output interface 54 to transmit data to each other. The processor 50 is an arithmetic processing unit such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The memory 52 is , for example, a memory such as a RAM (Random Access Memory) or a ROM (Read Only Memory). The input / output interface 54 includes an interface for acquiring information from an input device, an external device, an external server, a sensor, and the like, an interface for outputting information to an output device, an external device, an external server, and the like. The input device is, for example, a keyboard, a mouse, a microphone, or the like. The output device is, for example, a display, a speaker, a printer, a mailer, or the like. The processor 50 can issue a command to each module and perform a calculation based on the calculation result thereof.

The image processing unit 102 acquires the detection result (hereinafter, also referred to as vibration data) of the sensor 12 of the production equipment 10. Then, the image processing unit 102 performs FFT processing on the acquired vibration data, divides the frequency, and images the obtained vibration spectrum data to obtain image data. By these processes, the amount of data can be compressed, and the machine learning process and the discrimination process can be speeded up.

FIG. 8 is a flowchart showing an example of the detailed flow of the imaging process in step S101 of FIG. 7. First, the image processing unit 102 performs FFT processing on the measurement data of the sensor 12, then frequency-divides the measurement data (step S113), images the obtained vibration spectrum data, and outputs the image data (step S115). The image data obtained in step S115 is subjected to state analysis processing of the production equipment 10 using the discriminator 110 by the analysis unit 106 in step S105 of FIG.

The vibration measured by the vibration sensor includes a plurality of vibration waveforms composed of a plurality of factors. Generally, the measurement data detected from the vibration sensor is subjected to frequency analysis by performing a fast Fourier transform (FFT) process. By performing FFT processing on the measured data, a characteristic frequency (peak) is detected, and it is possible to diagnose an abnormality by determining whether the detected peak level is normal or abnormal by a threshold value.

As an example, the first discriminator 124 uses the frequency distribution obtained by FFT processing the measurement data of the vibration sensor to obtain the peak level of a specific frequency, the ratio of the maximum and average peak levels, and the signal-to-noise ratio (S / N ratio). -Noise ratio) and at least one of the integrated values of the peak level in a specific frequency range are obtained, a threshold is set for the obtained value, and abnormality judgment processing is performed based on whether or not it is within the threshold range. conduct. If it is within the threshold range, the first discriminator 124 determines that it is normal, and if it is outside the threshold range, it determines that it is abnormal.

The data determined to be "Unknown" by the second discriminator 126 may be referred to and analyzed by the operator. The result analyzed by the operator may be reflected in the threshold value of the abnormality determination processing of the detection result. The method of using the data determined to be "Unknown" will be described in detail in the embodiment described later.

As described above, in the present embodiment, when the abnormality determination unit 120 does not obtain the discrimination result in the abnormality determination process based on the detection result of the vibration sensor using the first discriminator 124, the vibration at that time. The detection result of the sensor is imaged by the image processing unit 102, and whether it is normal or "Unknown" is determined by the second discriminator 126. As described above, according to the present embodiment, the second discriminator 126 is machine-learned only for the detection result of the vibration sensor corresponding to the normal state, and normalizes the normal state. Even in the case of production where the manufacturing conditions are always fluid and it is difficult to accumulate information on failure events, the accuracy of discrimination can be improved.

In the present embodiment, the first discriminator 124 is used for the abnormality determination process of the production equipment 10, and the second discriminator 126 is not used for the abnormality determination process. Further, the discrimination result of the second discriminator 126 is used to update the first discriminant model 128 of the first discriminator 124, and this configuration will be described in the embodiment described later.

(Fifth Embodiment)
FIG. 14 is a functional block diagram showing a logical configuration of the analysis device 100 of the present embodiment. Analysis device 100 of the present embodiment extracts is not determined to be the normal data by state analyzing process of production equipment 10, configuration of updating the first determination model 128 of the first classifier 124 based on the data It is the same as the above-mentioned embodiment except that it has.

The operator inputs the correction information 30 of FIG. 15 in which the vibration characteristic information of the corresponding vibration is associated with the malfunction event specified by the operator to the analysis device 100 using an operation screen or the like. The generation unit 104 receives the input correction information 30, and updates the first discrimination model 128 of the first discriminator 124.

Further, since the second discriminator 126 is machine-learned only for the information in the normal state, the manufacturing conditions are always in flux and it is difficult to accumulate the information on the defective event in a small amount of many kinds or variable production of varieties. However, since the first determination model 128 can be updated, the accuracy of determining the abnormal state of the production equipment 10 can be improved.

(Example 1)
FIG. 20 is a flow chart for explaining the analysis device of the first embodiment.
First, when the vibration data is input from the sensor 12 of the production equipment 10, the abnormality determination unit 120 performs the FFT process in the first classifier 124 (step S11). At this time, the vibration characteristics are specified by pattern matching processing using the first discrimination model 128. The first discriminator 124 determines that the normal case in the range of the vibration characteristic threshold, if outside the range of the threshold value is determined to be abnormal. The abnormality determination unit 120 outputs this result to the equipment monitoring system 1 as an abnormality determination result of the production equipment 10 (not shown).

15. On the computer
Procedure for imaging the detection result of the vibration sensor installed in the production equipment,
A procedure for generating a discriminator by targeting the imaged data to machine learning processing,
A program for executing a procedure for performing a state analysis process of the production equipment using the discriminator.
16. 15. In the program described in
A procedure for performing a characteristic analysis process of vibration of the vibration sensor indicated by the detection result and performing an abnormality determination process of the production equipment using a threshold value.
A procedure for extracting data determined by the state analysis process that the production equipment is not in a normal state.
In the procedure for generating, the computer is further executed to receive the correction information based on the extracted data and update the threshold value.
The correction information is a program including information linking a malfunction event and a vibration characteristic.
17. On the computer
A procedure for performing abnormality determination processing of the production equipment using the first discriminator based on the detection result of the vibration sensor provided in the production equipment.
A procedure for imaging the detection result for which normality or abnormality could not be determined by the first discriminator.
A procedure for generating a second discriminator using the imaged data as a target of machine learning processing,
A program for executing a procedure for performing a state analysis process of the production equipment using the second discriminator.
18. 17. In the program described in
A procedure for extracting data determined by the state analysis process that the production equipment is not in a normal state.
In the procedure for generating, the computer is further made to execute the procedure for receiving the correction information based on the extracted data and updating the first discriminator.
The correction information is a program including information linking a malfunction event and a vibration characteristic.
19. 15. From 18. In the program described in any one of
A program for causing a computer to further perform a procedure of using the data determined to be normal by the state analysis process as teacher data of the machine learning process in the generated procedure.
20. 15. From 19. In the program described in any one of
Procedure for performing noise removal processing on the detection result,
A program for causing a computer to further perform a procedure for imaging the detection result after the noise removal process is performed in the imaging procedure.
21. 15. From 20. In the program described in any one of
The production equipment is a belt conveyor.
The vibration sensor is a program, which is a plurality of vibration sensors provided on the belt conveyor.

Claims (21)

Image processing means for imaging the detection results of vibration sensors installed in production equipment,
A generation means for generating a discriminator by using the imaged data as a target of machine learning processing,
An analysis device including an analysis means for performing a state analysis process of the production equipment using the discriminator. In the analysis apparatus according to claim 1,
A determination means for performing characteristic analysis processing of the vibration of the vibration sensor indicated by the detection result and performing abnormality determination processing for the production equipment using a threshold value.
Further provided with an extraction means for extracting data determined by the state analysis process that the production equipment is not in a normal state.
The generation means receives the correction information based on the extracted data, updates the threshold value, and receives the correction information.
The correction information is an analysis device including information linking a malfunction event and a vibration characteristic. Judgment means for performing abnormality determination processing of the production equipment using the first discriminator based on the detection result of the vibration sensor provided in the production equipment, and
An image processing means for imaging the detection result that could not be discriminated as normal or abnormal by the first discriminator, and
A generation means for generating a second discriminator using the imaged data as a target of machine learning processing,
An analysis means for performing a state analysis process of the production equipment using the second discriminator, and an analysis means.
To prepare
Analytical device. In the analysis apparatus according to claim 3,
Further provided with an extraction means for extracting data determined by the state analysis process that the production equipment is not in a normal state.
The generation means receives the correction information based on the extracted data, updates the first discriminator, and updates the first discriminator.
The correction information is an analysis device including information linking a malfunction event and a vibration characteristic. In the analysis apparatus according to any one of claims 1 to 4.
The generation means is an analysis device that uses the data determined to be normal by the state analysis process as teacher data for the machine learning process. In the analysis apparatus according to any one of claims 1 to 5,
Further provided with a processing means for performing noise removal processing on the detection result,
The image processing means is an analysis device that images the detection result after the noise removal processing by the processing means is performed. In the analysis apparatus according to any one of claims 1 to 6.
The production equipment is a belt conveyor.
The vibration sensor is an analysis device which is a plurality of vibration sensors provided on the belt conveyor. The analyzer is
The detection result of the vibration sensor installed in the production equipment is visualized and
A discriminator is generated by using the imaged data as a target of machine learning processing.
The state analysis process of the production equipment is performed using the discriminator.
analysis method. In the analysis method according to claim 8,
The analysis device further
The vibration characteristic analysis process of the vibration sensor indicated by the detection result is performed, and the abnormality determination process of the production equipment is performed using the threshold value.
Data determined by the state analysis process that the production equipment is not in a normal state is extracted, and the data is extracted.
The correction information based on the extracted data is received, the threshold value is updated, and the threshold value is updated.
The correction information is an analysis method including information linking a malfunction event and a vibration characteristic. The analyzer is
Based on the detection result of the vibration sensor installed in the production equipment, the abnormality determination process of the production equipment is performed using the first discriminator.
The detection result that could not be discriminated as normal or abnormal by the first discriminator was imaged and imaged.
A second discriminator is generated by using the imaged data as a target of machine learning processing.
The state analysis process of the production equipment is performed using the second discriminator.
analysis method. In the analysis method according to claim 10,
The analysis device further
Data determined by the state analysis process that the production equipment is not in a normal state is extracted, and the data is extracted.
The correction information based on the extracted data is received, and the first discriminator is updated.
The correction information is an analysis method including information linking a malfunction event and a vibration characteristic. In the analysis method according to any one of claims 8 to 11.
The analysis device further
An analysis method in which the data determined to be normal by the state analysis process is used as the teacher data of the machine learning process. In the analysis method according to any one of claims 8 to 12,
The analysis device further
Noise removal processing is performed on the detection result, and the noise is removed.
An analysis method for imaging the detection result after the noise removal processing is performed. In the analysis method according to any one of claims 8 to 13.
The production equipment is a belt conveyor.
The analysis method, wherein the vibration sensor is a plurality of vibration sensors provided on the belt conveyor. On the computer
Procedure for imaging the detection result of the vibration sensor installed in the production equipment,
A procedure for generating a discriminator by targeting the imaged data to machine learning processing,
A program for executing a procedure for performing an abnormality determination process of the production equipment using the discriminator. In the program of claim 15,
A procedure for performing a characteristic analysis process of vibration of the vibration sensor indicated by the detection result and performing an abnormality determination process of the production equipment using a threshold value.
A procedure for extracting data determined by the state analysis process that the production equipment is not in a normal state.
In the procedure for generating, the computer is further executed to receive the correction information based on the extracted data and update the threshold value.
The correction information is a program including information linking a malfunction event and a vibration characteristic. On the computer
A procedure for performing abnormality determination processing of the production equipment using the first discriminator based on the detection result of the vibration sensor provided in the production equipment.
A procedure for imaging the detection result for which normality or abnormality could not be determined by the first discriminator.
A procedure for generating a second discriminator using the imaged data as a target of machine learning processing,
A program for executing a procedure for performing a state analysis process of the production equipment using the second discriminator. In the program of claim 17,
A procedure for extracting data determined by the state analysis process that the production equipment is not in a normal state.
In the procedure for generating, the computer is further made to execute the procedure for receiving the correction information based on the extracted data and updating the first discriminator.
The correction information is a program including information linking a malfunction event and a vibration characteristic. In the program according to any one of claims 15 to 18.
A program for causing a computer to further perform a procedure of using the data determined to be normal by the state analysis process as teacher data of the machine learning process in the generated procedure. In the program according to any one of claims 15 to 19.
Procedure for performing noise removal processing on the detection result,
A program for causing a computer to further perform a procedure for imaging the detection result after the noise removal process is performed in the imaging procedure. In the program according to any one of claims 15 to 20,
The production equipment is a belt conveyor.
The vibration sensor is a program, which is a plurality of vibration sensors provided on the belt conveyor.

Images