WO2015114792A1 - Systeme, procede et programme d'assistance a l'entretien et a l'exploitation - Google Patents

Systeme, procede et programme d'assistance a l'entretien et a l'exploitation Download PDF

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Publication number
WO2015114792A1
WO2015114792A1 PCT/JP2014/052203 JP2014052203W WO2015114792A1 WO 2015114792 A1 WO2015114792 A1 WO 2015114792A1 JP 2014052203 W JP2014052203 W JP 2014052203W WO 2015114792 A1 WO2015114792 A1 WO 2015114792A1
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Prior art keywords
factor
maintenance
work machine
operating
operation support
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Ceased
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PCT/JP2014/052203
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English (en)
Japanese (ja)
Inventor
峻行 羽渕
巌 田沼
裕 吉川
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Hitachi Ltd
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Hitachi Ltd
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Priority to JP2015559687A priority Critical patent/JP6285467B2/ja
Priority to PCT/JP2014/052203 priority patent/WO2015114792A1/fr
Publication of WO2015114792A1 publication Critical patent/WO2015114792A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station

Definitions

  • the present invention relates to a maintenance operation support system for a work machine, a maintenance operation support method, and a maintenance operation support program.
  • Patent Document 1 JP-A-2002-181668
  • the life of a machine is estimated from the roughness of the road surface on which the mining machine travels, the traveling speed, and the load amount of the mining machine operated in the mine.
  • the sensor data is analyzed to estimate the life and the superiority of the surrounding environment.
  • Maintenance factors such as the implementation status of maintenance work on the machine can be cited as factors that affect the operating rate that are not described in Patent Document 1.
  • analysis of maintenance work logs input by maintenance workers can be mentioned.
  • the maintenance work log input by the maintenance worker at the site is inaccurate and may cause an input omission. Therefore, in order to analyze the maintenance work log and estimate the maintenance factor, a technique for complementing inaccuracy and input omission is required.
  • the present invention estimates and visualizes the status of maintenance work, which has conventionally been difficult to acquire information with sensors, without relying on maintenance work logs, and comprehensively evaluates factors that affect the operating rate. Objective.
  • a maintenance operation support apparatus that supports maintenance operation of a work machine is provided from one aspect of the present invention.
  • the maintenance operation support apparatus a communication unit that receives operation result data including operation time information of the work machine and sensor data measured by a sensor attached to the work machine, and a standard operation rate with respect to the accumulated operation time of the work machine
  • a storage unit that stores rated operating rate information including information regarding the sensor and sensor threshold information including information regarding a threshold with respect to a value indicated by the sensor data.
  • the processing unit calculates a rated operating rate achievement degree, which is information related to a ratio between the operating rate and the standard operating rate of the work machine, based on the operation result data and the rated operating rate information.
  • a maintenance factor that is a factor that affects the operating rate of a work machine and that is related to maintenance work on the work machine, based on the calculation of a certain environmental factor and based on the achievement rate of the rated operation rate, the operating factor, and the environmental factor Is calculated.
  • a maintenance operation support method in a maintenance operation support system that supports maintenance operation of a work machine by communicating with a sensor attached to the work machine via a network.
  • the maintenance operation support method receives operation result data including operation time information of the work machine and sensor data measured by the sensor, and includes information on the operation result data and the standard operation rate with respect to the accumulated operation time of the work machine.
  • sensor threshold information that calculates the rated availability achievement level, which is information related to the ratio between the availability and standard availability of the work machine, and includes sensor data and information on the threshold for the value indicated by the sensor data.
  • the feature amount is calculated based on the above, and based on the feature amount, the operating factor of the work machine is related to the operating factor and the operating environment of the working machine, which are factors affecting the operation rate of the work machine.
  • the environmental factors that are the factors are calculated, and the factors affecting the operating rate of the work machine based on the achievement rate of the rated operating rate, the operating factor, and the environmental factor.
  • To calculate the maintenance factor is a factor related to that maintenance work.
  • a maintenance operation support system including a sensor attached to a work machine, a work machine operation management system, a maintenance operation support device that supports maintenance operation of the work machine, and a terminal device.
  • the operation management system manages the operation result data including the operation time information of the work machine, and transmits the operation result data to the maintenance operation support device via the network.
  • the sensor transmits the measured sensor data to the maintenance operation support apparatus via the network.
  • the maintenance operation support apparatus includes rated operation rate information including information on a standard operation rate with respect to the cumulative operation time of the work machine, and sensor threshold information including information on a threshold value with respect to a value indicated by the sensor data.
  • the rated operating rate achievement level which is information on the operating rate and standard operating rate of the work machine, is calculated, and based on the sensor data and the sensor threshold information This is a factor that affects the operating rate of the work machine based on the feature value and that is related to the operation of the work machine and the operating environment of the work machine.
  • the environmental factors are calculated, and the factors affecting the operating rate of the work machine based on the degree of rated operating rate achievement, operating factors, and environmental factors.
  • Calculating the maintenance factor is a factor that, transmits information on maintenance factors, to the terminal apparatus via the network.
  • the terminal device displays the received information regarding the maintenance factor on the display unit.
  • maintenance factors can be analyzed and visualized based on existing sensor data. Therefore, the user of the present invention can examine measures for improving the operating rate based on the visualized maintenance factor.
  • the factors that affect the operating rate are represented by three factors: operating factors, environmental factors, and maintenance factors.
  • Operating factors are factors related to the operation of the work machine, such as the cylinder hydraulic pressure of the work machine, engine speed, and motor applied voltage
  • environmental factors are factors caused by the surrounding environment such as road surface conditions and ambient temperature
  • maintenance factors are maintenance work It is defined as a human factor resulting from maintenance work such as accuracy and periodic maintenance.
  • operation factors and environmental factors are digitized from sensors attached to the work machine, and maintenance personnel are estimated from the digitized operation factors and environmental factors.
  • the operating factor and the environmental factor are factors estimated by executing the principal component analysis based on the sensor data.
  • the maintenance factor is a factor estimated by performing factor analysis based on the operation factor, the environmental factor, and the operation rate. In this embodiment, it is allowed to use any value observable by the sensor, and the maintenance factor shown in FIG. 18 means a factor that cannot be observed by the sensor.
  • the present invention estimates and visualizes maintenance factors that are difficult to observe directly by principal component analysis and factor analysis based on sensor data and operating rates.
  • FIG. 1 is a diagram showing a network configuration example including a maintenance operation support system 100 that estimates and visualizes operation factors, environmental factors, and maintenance factors using the model of FIG. 18 in the present embodiment.
  • a maintenance operation support system 100 shown in FIG. 1 is a computer system for visualizing a maintenance situation and comprehensively evaluating factors that affect an operation rate.
  • a dump truck which is a large mining machine, will be described as an example of a work machine.
  • the operation rate of the dump truck is affected by operating factors related to the operation, environmental factors related to the surrounding environment, and maintenance factors related to the maintenance status.
  • the maintenance operation support system 100 of this embodiment visualizes these operation factors, environmental factors, and maintenance factors.
  • the maintenance operation support system 100 is connected to the network 160 and can communicate data with the mining machine 140, the operation management system 150, and the client terminal 170. Data measured by various sensors attached to the mining machine 140 is transmitted to the maintenance operation system 100 through the network 160. The operation status of the mining machine 140 collected by the operation management system 150 is transmitted to the maintenance operation system 100 via the network 160.
  • the client terminal 170 accesses the maintenance operation support system 100, and receives each process of receiving data input from the user using a keyboard, a mouse, and the like, and displaying the data obtained from the maintenance planning support system 100. Is responsible.
  • the hardware configuration of the maintenance operation support system 100 is as follows.
  • the maintenance operation support system 100 reads out a storage device 120 configured with an appropriate nonvolatile storage device such as a hard disk drive, a memory 113 configured with a volatile storage device such as a RAM, and a program held in the storage device 120 into the memory 113.
  • the CPU 114 (arithmetic unit) for performing overall control of the system itself and performing various determinations, computations and control processes, and the communication device 112 connected to the network 160 and responsible for communication processing with other devices.
  • the functions implemented in the storage device 120 include a rated operation rate achievement degree calculation function 124, a feature amount calculation function 126, an operation / environment factor estimation function 128, and a maintenance factor estimation function 130.
  • the example of the maintenance operation support system 100 shown in FIG. 1 illustrates such a function group and a database group that stores data used by each function.
  • the maintenance operation support system 100 may have an input / output function and a device (display, keyboard, etc.).
  • the maintenance operation support system 100 receives operation result data from the operation management system 150, compares the received information with the machine master database 122, and calculates a rated operation rate achievement level calculation function. 124.
  • the working rate of the work machine decreases as the cumulative operation time increases.
  • the rated operating rate achievement is a value obtained by correcting the influence of the cumulative operating time using the rated operating rate for each cumulative operating time.
  • the maintenance operation system 100 has a feature amount calculation function 126 that receives sensor data from the mining machine 140 and compares the received information with the machine master database 122 to calculate a feature amount.
  • the feature amount is a maximum value or average value of sensor data, a time of operation exceeding the rating, or the like, and is a characteristic value that is considered to particularly affect the operation rate.
  • the maintenance operation system 100 has an operation / environment factor analysis function 128 that compares the feature amount calculated by the above-described function with the machine master table 122 to estimate an operation factor and an environmental factor.
  • the maintenance operation system 100 has a maintenance factor estimation function 130 that calculates a maintenance factor from the rated availability rate calculated by the above-described function, an operation factor, and an environmental factor.
  • FIG. 3 shows an example of the operation result database 121 in this embodiment.
  • the operation result database 121 is a database in which operation results of mining machines are accumulated.
  • the data structure is a set of records including a cumulative operation time 202, an operation time 203, a planned stop time 204, an unplanned stop time 205, and an operation rate 206 using the machine ID 201 and the period 207 as keys. .
  • the above-mentioned machine ID 201 stores an ID for uniquely identifying a mining machine.
  • the period 207 the calculation period of each record is stored.
  • the accumulated operation time 202 stores the accumulated operation time at the end of the period of the machine.
  • the operation time 203 includes the operation time of the machine during the period
  • the planned stop time 204 includes the planned stop time of the machine during the period
  • the unplanned stop time 205 includes the machine during the period.
  • the planned stop means a stop of the machine due to predetermined periodic maintenance or the like
  • the unplanned stop means a stop of the machine due to a sudden failure or the like.
  • the operation rate 206 stores an operation rate represented by operation time / (operation time + planned stop time + unplanned stop time) ⁇ 100.
  • the period 207 is divided by month, but an arbitrary value may be stored.
  • FIG. 4 shows an example of the rated operation rate table 210 in the machine master database 122 in the present embodiment.
  • the rated operating rate table 210 is a database in which standard operating rates of mining machines are accumulated.
  • the data structure is a collection of records including the rated operation rate 213 with the model name 211 and the accumulated operation time 212 as keys.
  • the accumulated operating time 212 stores a category of the accumulated operating time of the machine.
  • the rated operation rate 213 stores an operation rate that is standard when a certain model has a certain accumulated operation time.
  • the rated operating rate 213 is calculated from the interval between periodic machine maintenance and parts replacement, and is generally provided by a manufacturer or a vendor.
  • FIG. 5 shows an example of the machine specification table 220 in the machine master database 122.
  • the machine specification table 220 stores machine specifications.
  • the data structure is a collection of records consisting of a site ID 222, a customer ID 223, a model name 224, a vehicle body mass 225, a total vehicle mass 226, and a TKPH (Ton ⁇ Km Per Hour) 227 with the machine ID 221 as a key. It is.
  • the machine ID 221 described above stores an ID that uniquely identifies the machine.
  • the site ID 222 and the customer ID 223 store an ID that uniquely identifies the site where the machine operates and an ID that uniquely identifies the customer who owns the machine.
  • the model name 224 stores a character string for specifying the model.
  • the vehicle body mass 225 stores the mass of the machine body.
  • the total vehicle mass 226 stores the maximum rated value of the sum of the vehicle mass 225 and the mass of the load.
  • TKPH refers to the effects of the load (Ton), speed (Km), and outside temperature on the tire.
  • TKPH is the sum of the vehicle mass 225 and the mass of the load (Ton), and the machine operating speed.
  • the rated maximum value obtained by multiplying (Km) is stored.
  • the vehicle body mass 225, the total vehicle mass 226, and the TKPH 227 are specifications generally provided by a manufacturer.
  • FIG. 6 shows an example of the sensor data database 123.
  • the sensor data database 123 stores sensor data collected from the mining machine 140 via the network 160.
  • the data structure is a set of records composed of n pieces of sensor data 304 with the machine ID 301, the date 302, and the time 303 as keys.
  • the machine ID 301 stores an ID that uniquely identifies the machine.
  • the year / month / day 302 and the time 303 respectively store the date / time when the data 304 was acquired and the time.
  • the sensor data 304 stores various sensor data collected via the network 160.
  • Fig. 7 shows the relationship between the maximum, average, and rated operating time of sensor data (loading capacity).
  • the maximum and average are the maximum value and average value of sensor data within a specified period.
  • the operating time above the rated value means the sum of the operating time exceeding the rated value within a specified period.
  • the maximum and average values represented by the rating ratio and the operation time exceeding the rating are handled as the feature amount.
  • FIG. 8 shows an example of the rated availability achievement level database 125.
  • the rated operation rate achievement degree database 125 accumulates the ratio between the standard operation rate obtained from the accumulated operation time and the actual operation rate.
  • the data structure is a set of records composed of [operating rate] rated operating rate achievement degree 402 with machine ID 401 and period 403 as keys.
  • the machine ID 401 stores an ID that uniquely identifies the machine.
  • the period 403 stores a period for calculating the [operation rate] rated operation rate achievement level 402.
  • [Occupancy rate] Rated operation rate achievement degree 402 stores the value obtained by dividing the operation rate 206 of the machine for the relevant period by the rated operation rate 213 and multiplying it by 100, as the rated operation rate achievement degree.
  • the rated operating rate achievement level is calculated by the rated operating rate achievement level calculating function 124.
  • FIG. 9 shows an example of the load amount table 500 in the feature amount database 127.
  • the load amount table 500 stores the feature amount of the load amount extracted by the feature amount calculation function 126 from the load amount data stored in the sensor database 123.
  • the data structure is a set of records consisting of a machine ID 501 and a period 505 as a key, [loading capacity] rated ratio maximum 502, [loading capacity] rated ratio average 503, and [loading capacity] rated time or more operation time 504. It is.
  • the machine ID 501 stores an ID that uniquely identifies the machine.
  • the period 505 stores a period during which the [loading capacity] rating ratio maximum 502, the [loading capacity] rating ratio average 503, and the [loading capacity] rating or more operation time 504 are calculated.
  • the [loading capacity] rating ratio maximum 502 the [loading capacity] rating ratio average 503, and the [loading capacity] rating or more operation time 504, values obtained by extracting the feature values shown in FIG. Stored. Note that the feature amount extraction is executed by the feature amount calculation function 126.
  • FIG. 10 shows an example of the TKPH table 510 in the feature amount database 127.
  • the TKPH table 510 stores the feature amount of the load amount extracted by the feature amount calculation function 126 from the TKPH data stored in the sensor data database 123.
  • the data structure is a set of records including machine ID 511, period 515 as a key, [TKPH] rated ratio maximum 512, [TKPH] rated ratio average 513, and [TKPH] rated or more operating time 514. It has the same structure as the load amount table 500.
  • FIG. 11 shows an example of the vibration level table 600 in the feature amount database 127.
  • the vibration level table 600 stores the vibration level feature quantity extracted by the feature quantity calculation function 126 from the vibration level data stored in the sensor data database 123.
  • the data structure includes [machine vibration level] width direction maximum 602, [vibration level] width direction average 603, [vibration level] circumferential direction maximum 604, [vibration level] using machine ID 601 and period 606 as keys. This is an aggregate of records composed of an average 605 in the circumferential direction.
  • the structure is mainly the same as that of the load amount table 500 and the TKPH table 510, but in this embodiment, since the vibration level rating is not set, there is no operation time exceeding the rating.
  • the direction of vibration is divided into the tire width direction (602, 603) and the circumferential direction (604, 605).
  • the load amount, TKPH, and vibration level which are generally said to have a large influence on the operation rate, are shown as examples.
  • any data can be used as long as the data can be collected by the sensor. Good.
  • each sensor data may be normalized.
  • FIG. 12 shows an example of the operation / environment factor database 129.
  • the operation / environment factor database 129 stores the operation factor of each machine and the environment factor estimated from the feature amount database 127 by the operation / environment factor analysis function 128.
  • the data structure is composed of a set of records including an operation factor 702, an environment factor 703, a site ID 704, and a customer ID 705 with the machine ID 701 and the period 706 as keys.
  • the machine ID 701 stores an ID that uniquely identifies the machine.
  • the period 706 stores a period for calculating the operating factor 702 and the environmental factor 703.
  • the operation factor 702 and the environment factor 703 store values obtained by calculating the operation factor related to the operation and the environment factor related to the surrounding environment during the period 706, respectively.
  • the operation factor 702 and the environment factor 703 are obtained by executing principal component analysis based on each feature quantity in the feature quantity database 127.
  • the site ID 704 stores an ID that uniquely identifies the site where the machine operates.
  • the customer ID 705 stores an ID that uniquely identifies the customer who owns the machine.
  • FIG. 13 shows an example of the maintenance factor database 131.
  • the maintenance factor database 131 stores maintenance factors estimated from the rated operating rate achievement database 125 and the operation / environment factor database 129 by the maintenance factor estimation function 130.
  • the data structure is composed of a collection of records including an environmental factor 712, a site ID 713, and a customer ID 714 with the machine ID 711 and the period 716 as keys.
  • the machine ID 711 stores an ID that uniquely identifies the machine.
  • the period 715 stores a period for calculating the maintenance factor 712.
  • the maintenance factor 712 stores a factor related to the maintenance status that affects the operation rate.
  • the site ID 713 stores an ID that uniquely identifies the site where the machine operates.
  • the customer ID 714 stores an ID that uniquely identifies the customer who owns the machine.
  • FIG. 14 is an overall flowchart for explaining the processing procedure of the present embodiment.
  • the maintenance operation system 140 acquires various sensor data from the mining machine 140 (S1401).
  • the maintenance operation system 100 collates the acquired sensor data with the machine master database and calculates the feature amount (S1402).
  • the maintenance operation system 140 acquires operation result data of the mining machine 140 from the operation management system 150 (S1403).
  • the maintenance operation management system 100 collates the acquired operation result data with the machine master database, and calculates the rated operation rate achievement level (S1404).
  • the maintenance operation management system 100 compares the calculated feature quantity with the machine master table, and calculates an operation factor and an environmental factor (S1405).
  • the maintenance operation system 100 calculates a maintenance factor from the calculated rated operating rate achievement level, an operation factor, and an environmental factor (S1406).
  • the maintenance operation system 100 receives an inquiry about data related to the maintenance factor from the client terminal 170 (S1407), and transmits data related to the maintenance factor to the client terminal 170 (S1408).
  • the client terminal 170 outputs the received data related to the maintenance factor to the display unit (S1409). Output images are shown in FIGS.
  • FIG. 15 is a flowchart showing an example of a processing procedure of a maintenance operation support method for estimating and visualizing operation factors, environmental factors, and maintenance factors using the model of FIG. 18 in the present embodiment.
  • operation results, machine masters, and sensor data are input, and operating factors, environmental factors, and maintenance factors that affect the operating rate are estimated and visualized.
  • the process S801 is executed by the rated operation rate achievement degree calculation function 124.
  • the rated operation rate achievement level calculation function 124 refers to the operation result database 121 and the rated operation rate table 210 of the machine master database 122, and the machine ID 201, the accumulated operation time 202, and the machine specification table 220 of the operation result database 121.
  • the model name 224 is stored in the memory 112.
  • the rated operating rate achievement level calculation function 124 refers to the rated operating rate table 210 using the machine ID 201, the accumulated operating time 202, and the model name 224, and stores the rated operating rate 213 of the machine in the memory 112.
  • the rated operation rate achievement degree calculation function 124 calculates the rated operation based on the operation rate / rated operation rate ⁇ 100 from the rated operation rate 213 of the machine stored in the memory 112 and the operation rate 206 stored in the operation result database 121.
  • the rate achievement is calculated and stored in the memory 112.
  • the rated operation rate achievement calculation function 124 in process S802 converts the rated operation rate achievement stored in the memory 112 in process S801 into the [operation rate] rated operation rate achievement degree 402 of the rated operation rate achievement database 125. Store.
  • step S803 is executed by the feature amount calculation function 126.
  • the feature amount calculation 126 refers to the operation result database 121, the machine specification table 220 of the machine master database 122, and the sensor data database 123, and the machine ID 201, the period 207, and the machine specification table 220 of the operation result database 121.
  • the vehicle body mass 225, the total vehicle mass 226, TKPH 227, and sensor data 304 of the sensor data database 123 are stored in the memory 112.
  • the feature amount calculation function 126 calculates the maximum value, the average value, and the operation time exceeding the rating shown in FIG. 3A from the vehicle body mass, the total vehicle mass, the rated value such as TKPH, and the sensor data stored in the memory 112. And stored in the memory 112.
  • the feature amount calculation function 126 normalizes the maximum value and the average value stored in the memory 112 using each rated value, and stores the normalized value in the memory 112 as the maximum rated ratio and the average rated ratio.
  • the feature amount is calculated using the load amount, TKPH, and vibration level, which are generally said to have a large effect on the operation rate. Any data may be used. In addition, each sensor data may be normalized.
  • step S804 the feature amount calculation function 126 stores the feature amount stored in the memory 112 in step S803 in each table of the feature amount database 127 in association with the machine ID and the period.
  • the operation / environment factor analysis function 128 refers to the machine specification table 220 of the feature quantity database 127 and the machine master database 122, and each feature quantity, machine ID, period, and machine specification table 220 from the feature quantity database 127.
  • the machine site ID 222 and the customer ID 223 are read and stored in the memory 112.
  • the operation / environment factor analysis function performs principal component analysis based on each feature amount, calculates the obtained principal component as an operation factor and an environment factor, and stores it in the memory 112.
  • the operation factor refers to a principal component having a strong correlation with the feature amount resulting from the operation such as the loading amount or TKPH
  • the environmental factor is a principal component having a strong correlation with the feature amount due to the surrounding environment such as a vibration level depending on the road surface condition. Point to.
  • the operation / environment factor analysis function 128 in the process S806 the operation factor stored in the memory 112 in the process S805, the environment factor, the machine ID read from the feature amount database 127, the period, and the machine spec table 220
  • the read site ID and customer ID are stored in the operation / environment factor database 129.
  • step S807 is executed by the maintenance factor estimation function 130.
  • the maintenance factor estimation function 130 refers to the rated operating rate achievement database 125 and the operation / environment factor database 129, and from the rated operating rate achievement database 125, the machine ID 401, the period 403, and the [operating rate] rated operating rate are achieved.
  • the operation factor 702, the environment factor 703, the site ID 704, and the customer ID 705 are read from the operation / environment factor database 129 and stored in the memory 112.
  • the maintenance factor estimation function 130 performs factor analysis using the [operation rate] rated operation rate achievement degree 402, the operation factor 702, and the environmental factor 703, and operates other than the operation factor 702 and the environmental factor 703. Factors affecting the rate are estimated and stored in the memory 112 as maintenance factors.
  • the operating factor 702 and the environmental factor 703 are factors obtained from arbitrary sensor data, and a maintenance factor is defined as a factor that is not transferred to these factors.
  • a maintenance factor is defined as a factor that is not transferred to these factors. The relationship between the factors will be described in detail with reference to FIG.
  • the analysis may be performed by adding the factor caused by the unmeasurement.
  • the operating factor obtained by the principal component analysis or the factor having a strong correlation with the environmental factor is used as the operating factor or the environmental factor due to unmeasurement, respectively.
  • step S808 the maintenance factor estimation function 130 stores the maintenance factor, machine ID, period, site ID, and customer ID stored in the memory 112 in step S807 in the maintenance factor database 131.
  • FIG. 16 and 17 are examples of output results of this embodiment.
  • the customer ID 714 is plotted on the horizontal axis, and the maintenance factor 712 is plotted.
  • Maintenance operation support system 111 I / O (output device) 112 Communication device 113 Memory 114 CPU (arithmetic unit) DESCRIPTION OF SYMBOLS 120 Storage device 121 Operation performance database 122 Machine master database 123 Sensor data database 124 Rated operation rate achievement degree calculation function 125 Rated operation rate achievement degree database 126 Feature amount calculation function 127 Feature amount database 128 Operation / environmental factor analysis function 129 Operation / environment Factor database 130 Maintenance factor estimation function 131 Maintenance factor database 140 Mining equipment (work machine) 150 Operation management system 160 Network 170 Client terminal

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Abstract

L'invention permet de visualiser des facteurs d'entretien ayant un impact sur la vitesse de fonctionnement. Les facteurs d'entretien ne pouvant pas être détectés peuvent être visualisés par le calcul de valeurs caractéristiques telles que des valeurs maximum et des valeurs moyennes associées à la capacité de charge, à la vitesse, à l'accélération et analogue, provenant de données de capteur correspondantes, par la spécification de facteurs fonctionnels et d'environnement obtenus par l'exécution d'une analyse de composants principaux sur la base des valeurs caractéristiques ; par le calcul, à partir de la performance de fonctionnement et de la vitesse de fonctionnement assignée, du niveau atteint de vitesse de fonctionnement assignée ayant été corrigé par rapport au temps d'exploitation cumulé, et par l'exécution d'une analyse de facteurs sur la base des facteurs fonctionnels, des facteurs d'environnement et du niveau atteint de vitesse de fonctionnement assignée.
PCT/JP2014/052203 2014-01-31 2014-01-31 Systeme, procede et programme d'assistance a l'entretien et a l'exploitation Ceased WO2015114792A1 (fr)

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JP2015559687A JP6285467B2 (ja) 2014-01-31 2014-01-31 保守運用支援システム、保守運用支援方法、保守運用支援プログラム
PCT/JP2014/052203 WO2015114792A1 (fr) 2014-01-31 2014-01-31 Systeme, procede et programme d'assistance a l'entretien et a l'exploitation

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