CN117391663A

CN117391663A - Equipment maintenance methods, devices, non-volatile storage media and computer equipment

Info

Publication number: CN117391663A
Application number: CN202311220843.8A
Authority: CN
Inventors: 张潇; 丁建武; 黄楠; 曹玲玲; 纪利军; 李多娇; 解晓东; 衣书伟; 赵璧; 董光哲; 闫付乐
Original assignee: State Grid Beijing Electric Power Co Ltd; State Grid Corp of China SGCC
Current assignee: State Grid Beijing Electric Power Co Ltd; State Grid Corp of China SGCC
Priority date: 2023-09-20
Filing date: 2023-09-20
Publication date: 2024-01-12

Abstract

The invention discloses an equipment maintenance method, device, non-volatile storage medium and computer equipment. Among them, the method includes: obtaining the connection relationship between multiple power transmission and transformation equipment and the electrical parameters of the multiple power transmission and transformation equipment; constructing multiple power transmission and transformation equipment according to the connection relationship between the multiple power transmission and transformation equipment A fault tree model, where the fault tree model is used to characterize the causes and consequences of failures of multiple power transmission and transformation equipment; according to the fault tree model, the first probability of failure of multiple power transmission and transformation equipment is determined; according to multiple power transmission and transformation equipment The electrical parameters of the electrical equipment are used to determine the second probability of failure of multiple power transmission and transformation equipment; based on the first probability and the second probability, the multiple power transmission and transformation equipment are inspected and repaired. The present invention solves the technical problem of being unable to conduct timely maintenance when the risk of failure is high due to the inability to fully understand the operating status of the equipment in related technologies.

Description

Equipment maintenance method and device, nonvolatile storage medium and computer equipment

Technical Field

The invention relates to the technical field of equipment overhaul, in particular to an equipment overhaul method, an equipment overhaul device, a nonvolatile storage medium and computer equipment.

Background

Conventional power transmission and transformation overhaul methods are generally based on experience and rely on manual judgment and operation. However, the complexity and large scale of the power system makes it difficult for conventional methods to comprehensively and accurately assess the health status of the equipment, especially in terms of real-time monitoring and big data analysis. Therefore, the device cannot be intervened in time before a potential problem occurs in the device, and the countermeasure is often needed after the fault occurs, so that the power failure time is too long, and the stability of power supply is affected. Simple service methods tend to be difficult to effectively cope with the diverse failure modes of the equipment. The fault of the power transmission and transformation equipment may relate to multiple aspects such as electricity, machinery, heat and the like, and the traditional method may only focus on a certain aspect, neglect the overall performance, so that the running state of the equipment cannot be comprehensively known, the fault risk cannot be timely overhauled and removed under the condition of higher fault risk, and the reliable running of the equipment cannot be ensured.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the invention provides a device maintenance method, a device, a nonvolatile storage medium and computer equipment, which at least solve the technical problem that the equipment operation state cannot be comprehensively known in the related art, so that the maintenance cannot be timely performed under the condition of higher fault risk.

According to an aspect of an embodiment of the present invention, there is provided an equipment servicing method, including: acquiring connection relations among a plurality of power transmission and transformation devices and electrical parameters of the plurality of power transmission and transformation devices; constructing a fault tree model of a plurality of power transmission and transformation devices according to the connection relation among the plurality of power transmission and transformation devices, wherein the fault tree model is used for representing the reasons and results of the faults of the plurality of power transmission and transformation devices; determining a first probability of a plurality of power transmission and transformation devices to fail according to the fault tree model; determining a second probability of failure of the plurality of power transmission and transformation devices according to the electrical parameters of the plurality of power transmission and transformation devices; and overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

Optionally, constructing a fault tree model of the multiple power transmission and transformation devices according to the connection relationship between the multiple power transmission and transformation devices, including: generating a top node of a fault tree model according to a predetermined fault event to be analyzed; generating a plurality of basic nodes of a fault tree model according to a plurality of basic events respectively, wherein the plurality of basic events are events which lead to the occurrence of the fault event to be analyzed; determining the connection relation between a plurality of basic nodes and the connection relation between the plurality of basic nodes and the top node according to the connection relation between a plurality of power transmission and transformation devices; and constructing a fault tree model according to the connection relation among the plurality of basic nodes and the connection relation between the plurality of basic nodes and the top node.

Optionally, determining a connection relationship between a plurality of basic nodes and a top node according to a connection relationship between a plurality of power transmission and transformation devices, and constructing a fault tree model, including: determining father-son relations among a plurality of basic nodes according to connection relations among a plurality of power transmission and transformation devices, wherein the father-son relations among the plurality of basic nodes represent event occurrence corresponding to child nodes and cause event occurrence corresponding to father nodes; determining a connection relationship among a plurality of basic nodes according to father-son relationships among the plurality of basic nodes; determining father-son relations between a plurality of basic nodes and top nodes according to connection relations among a plurality of power transmission and transformation devices, wherein the top nodes are connected with basic nodes corresponding to basic events which directly cause faults to be analyzed; and determining the connection relation between the plurality of basic nodes and the top node according to the parent-child relation between the plurality of basic nodes and the top node.

Optionally, constructing the fault tree model according to the connection relation between the plurality of basic nodes and the top node includes: determining the attribute of a connecting edge between a plurality of basic nodes and a top node according to the connection relation among a plurality of power transmission and transformation devices, wherein the attribute of the connecting edge represents that the occurrence of an event corresponding to a child node singly leads to the occurrence of an event corresponding to a corresponding parent node, or the occurrence of the event corresponding to a plurality of child nodes jointly leads to the occurrence of the event corresponding to the corresponding parent node; and connecting the plurality of basic nodes with the top node according to the attribute of the connecting edges between the plurality of basic nodes and the top node, the connection relation between the plurality of basic nodes and the top node, and constructing a fault tree model.

Optionally, determining, according to the fault tree model, a first probability that the plurality of power transmission and transformation devices fail includes: determining the occurrence probability of events corresponding to a plurality of basic nodes; determining the occurrence probability of a fault event to be analyzed corresponding to the top node according to the attribute of the connecting edges between the plurality of basic nodes and the top node; and determining a first probability of the faults of the power transmission and transformation equipment according to the probability of the faults to be analyzed.

Optionally, determining the second probability of the plurality of power transmission and transformation devices failing according to the electrical parameters of the plurality of power transmission and transformation devices includes: and inputting the electrical parameters of the power transmission and transformation devices into a predetermined time sequence prediction model, and outputting a second probability by using the time sequence prediction model, wherein the time sequence prediction model is obtained by training a plurality of groups of training samples, and each group of samples in the plurality of groups of training samples comprises historical electrical parameters of the power transmission and transformation devices in a historical time period and the probability of faults in the historical time period.

Optionally, overhauling the plurality of power transmission and transformation devices according to the first probability and the second probability, including: determining the comprehensive probability of faults of a plurality of power transmission and transformation devices according to the first probability and the second probability; determining a scheme for overhauling a plurality of power transmission and transformation equipment according to the comprehensive probability; and overhauling the plurality of power transmission and transformation equipment according to the overhauling scheme of the plurality of power transmission and transformation equipment.

According to another aspect of the embodiment of the present invention, there is also provided an equipment servicing device, including: the acquisition module is used for acquiring the connection relation among the plurality of power transmission and transformation devices and the electrical parameters of the plurality of power transmission and transformation devices; the modeling module is used for constructing fault tree models of the power transmission and transformation devices according to the connection relation among the power transmission and transformation devices, wherein the fault tree models are used for representing the reasons and results of the faults of the power transmission and transformation devices; the first determining module is used for determining a first probability of failure of the power transmission and transformation equipment according to the failure tree model; the second determining module is used for determining a second probability of failure of the power transmission and transformation equipment according to the electrical parameters of the power transmission and transformation equipment; and the overhaul module is used for overhauling the power transmission and transformation equipment according to the first probability and the second probability.

According to still another aspect of the embodiments of the present invention, there is further provided a nonvolatile storage medium, the nonvolatile storage medium including a stored program, wherein when the program runs, a device in which the nonvolatile storage medium is controlled to execute any one of the above-mentioned device maintenance methods.

According to still another aspect of the embodiment of the present invention, there is further provided a computer device, where the computer device includes a processor, and the processor is configured to execute a program, where the program executes any one of the foregoing method for repairing equipment.

In the embodiment of the invention, the connection relation among a plurality of power transmission and transformation devices and the electrical parameters of the plurality of power transmission and transformation devices are obtained; constructing a fault tree model of a plurality of power transmission and transformation devices according to the connection relation among the plurality of power transmission and transformation devices, wherein the fault tree model is used for representing the reasons and results of the faults of the plurality of power transmission and transformation devices; determining a first probability of a plurality of power transmission and transformation devices to fail according to the fault tree model; determining a second probability of failure of the plurality of power transmission and transformation devices according to the electrical parameters of the plurality of power transmission and transformation devices; according to the first probability and the second probability, the plurality of power transmission and transformation equipment is overhauled, the purpose that the current electrical parameters of the plurality of power transmission and transformation equipment and the connection condition of the plurality of power transmission and transformation equipment are considered simultaneously to determine the risk of the current plurality of power transmission and transformation equipment to fail is achieved, so that the running state of the equipment is comprehensively known, the probability of the plurality of power transmission and transformation equipment to fail is accurately and timely predicted, the technical effect of overhauling the plurality of power transmission and transformation equipment is achieved according to the probability, and the technical problem that the equipment cannot be overhauled in time under the condition of higher failure risk due to the fact that the running state of the equipment cannot be comprehensively known in the related art is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:

FIG. 1 shows a block diagram of the hardware architecture of a computer terminal for implementing an equipment servicing method;

FIG. 2 is a schematic flow chart of an equipment servicing method provided according to an embodiment of the present invention;

fig. 3 is a block diagram of an apparatus for service according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In accordance with an embodiment of the present invention, there is provided an embodiment of a method of servicing an apparatus, it being noted that the steps shown in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than what is shown or described herein.

The method embodiment provided in the first embodiment of the present application may be executed in a mobile terminal, a computer terminal or a similar computing device. Fig. 1 shows a block diagram of a hardware architecture of a computer terminal for implementing an equipment servicing method. As shown in fig. 1, the computer terminal 10 may include one or more (shown as 102a, 102b, … …,102 n) processors (which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors and/or other data processing circuits described above may be referred to herein generally as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module or incorporated, in whole or in part, into any of the other elements in the computer terminal 10. As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).

The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the equipment maintenance method in the embodiment of the present invention, and the processor executes the software programs and modules stored in the memory 104, thereby executing various functional applications and data processing, that is, implementing the equipment maintenance method of the application program. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10.

Fig. 2 is a schematic flow chart of an equipment maintenance method according to an embodiment of the present invention, as shown in fig. 2, the method includes the following steps:

step S202, obtaining connection relationships among a plurality of power transmission and transformation devices and electrical parameters of the plurality of power transmission and transformation devices.

In this step, the plurality of power transmission and transformation devices may be power devices located in the same area, and various connection relationships exist between the plurality of power transmission and transformation devices. The electrical parameters of the plurality of power transmission and transformation devices can comprise electrical data such as voltage, current, power and the like of the devices at the current moment. In this patent, a plurality of power transmission and transformation devices may refer to not only large-sized and highly integrated power devices, but also a certain module or a certain element in the devices.

And step S204, constructing a fault tree model of the power transmission and transformation devices according to the connection relation among the power transmission and transformation devices, wherein the fault tree model is used for representing the reasons and results of the faults of the power transmission and transformation devices.

Step S206, determining a first probability of failure of the power transmission and transformation devices according to the failure tree model.

The fault tree model (Fault Tree Analysis, FTA for short) is a reliability tool for analyzing and predicting the occurrence of system faults. The method is a qualitative and quantitative reliability analysis method and can be used for evaluating the safety and reliability of the system. The fault tree model can help identify various events in the system that may lead to a fault, and build a tree structure through logical relationships to represent the likelihood of the fault occurring. In the two steps, various events which can cause faults of the power transmission and transformation equipment can be determined through the connection relation among the power transmission and transformation equipment, a fault tree model of the power transmission and transformation equipment is built, and then the first probability of faults of the power transmission and transformation equipment is further determined.

Step S208, determining a second probability of failure of the power transmission and transformation devices according to the electrical parameters of the power transmission and transformation devices.

In this step, the electrical parameters of the plurality of power transmission and transformation devices at the current moment may be analyzed, and the second probability of failure of the plurality of power transmission and transformation devices may be determined according to whether the electrical parameters are abnormal. It should be noted that the first probability and the second probability are probabilities of multiple power transmission and transformation devices determined from different angles, specifically, the first probability is based on determining various events possibly occurring in the devices and causing faults, analyzing actual connection relations of the multiple power transmission and transformation devices, determining the probability that the multiple power transmission and transformation devices possibly occur faults according to the logic relations, that is, when determining the first probability, the situation that the multiple power transmission and transformation devices are not personalized is not involved, only the probability that the devices of the type fail under the current connection relation is considered, for example, the first probability is the probability that a certain type of transformer fails, but the working condition of the specific transformer for carrying out power transmission and transformation at present is not considered; the second probability is determined according to the electrical parameters of the power transmission and transformation devices, that is, the second probability relates to the individual situation of the power transmission and transformation devices, and the probability of the faults of the power transmission and transformation devices is determined according to the working situation of the specific transformer for carrying out power transmission and transformation at present.

And step S210, overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

In the step, the first probability and the second probability obtained in the two modes can be comprehensively considered, the risk of the failure of the current power transmission and transformation devices is determined, and the power transmission and transformation devices are overhauled according to the risk.

Through the steps, the purpose of determining the risk of the failure of the current power transmission and transformation equipment by considering the current electrical parameters of the power transmission and transformation equipment and the connection condition of the power transmission and transformation equipment can be achieved, so that the running state of the equipment is comprehensively known, the probability of the failure of the power transmission and transformation equipment is accurately and timely predicted, the technical effect of overhauling the power transmission and transformation equipment is achieved, and the technical problem that the overhauling cannot be timely performed under the condition of higher failure risk due to the fact that the running state of the equipment cannot be comprehensively known in the related art is solved.

As an alternative embodiment, constructing a fault tree model of a plurality of power transmission and transformation devices according to a connection relationship between the plurality of power transmission and transformation devices includes: generating a top node of a fault tree model according to a predetermined fault event to be analyzed; generating a plurality of basic nodes of a fault tree model according to a plurality of basic events respectively, wherein the plurality of basic events are events which lead to the occurrence of the fault event to be analyzed; determining the connection relation between a plurality of basic nodes and the connection relation between the plurality of basic nodes and the top node according to the connection relation between a plurality of power transmission and transformation devices; and constructing a fault tree model according to the connection relation among the plurality of basic nodes and the connection relation between the plurality of basic nodes and the top node.

Alternatively, possible fault events may be identified based on the connection relationships between the devices and known fault patterns, with the primary device elements and possible fault events listed as nodes of the fault tree. One or more final fault events can be selected as the fault event to be analyzed, and the fault event to be analyzed is taken as the top node of the fault tree, so that the fault event to be analyzed is taken as the basic node of the fault tree. And then, according to the connection relation among the power transmission and transformation devices, the causal relation among the fault events can be determined, and then, the connection relation between the basic nodes and the top nodes can be determined.

As an optional embodiment, determining a connection relationship between a plurality of basic nodes and a top node according to a connection relationship between a plurality of power transmission and transformation devices, and constructing a fault tree model includes: determining father-son relations among a plurality of basic nodes according to connection relations among a plurality of power transmission and transformation devices, wherein the father-son relations among the plurality of basic nodes represent event occurrence corresponding to child nodes and cause event occurrence corresponding to father nodes; determining a connection relationship among a plurality of basic nodes according to father-son relationships among the plurality of basic nodes; determining father-son relations between a plurality of basic nodes and top nodes according to connection relations among a plurality of power transmission and transformation devices, wherein the top nodes are connected with basic nodes corresponding to basic events which directly cause faults to be analyzed; and determining the connection relation between the plurality of basic nodes and the top node according to the parent-child relation between the plurality of basic nodes and the top node.

Alternatively, for each fault event, its cause and effect of the fault is determined and the logical relationship between the cause and effect is represented as a branch of the fault tree, i.e. the fault events, causes and effects may be associated in a tree structure. Each fault event acts as a node of the tree, and causes and effects are connected as branches to the corresponding event node.

As an alternative embodiment, constructing a fault tree model according to a connection relationship between a plurality of basic nodes and a top node includes: determining the attribute of a connecting edge between a plurality of basic nodes and a top node according to the connection relation among a plurality of power transmission and transformation devices, wherein the attribute of the connecting edge represents that the occurrence of an event corresponding to a child node singly leads to the occurrence of an event corresponding to a corresponding parent node, or the occurrence of the event corresponding to a plurality of child nodes jointly leads to the occurrence of the event corresponding to the corresponding parent node; and connecting the plurality of basic nodes with the top node according to the attribute of the connecting edges between the plurality of basic nodes and the top node, the connection relation between the plurality of basic nodes and the top node, and constructing a fault tree model.

Alternatively, the edges connecting the plurality of nodes may have respective attributes, and the attributes of the edges may represent causal relationships of occurrence of events corresponding to the nodes, that is, the attributes of the edges may represent logical relationships between events corresponding to the nodes, which may be "and", "or", or the like. For logical operations in branching relationships, boolean algebra may be used to represent. For example, the AND relationship is expressed as a logical AND operation, using AND operators; the OR relationship is represented as a logical OR operation, using an OR operator. For example: assuming transformer fault tree analysis is to be performed, identifying possible fault events such as transformer overload, insulation breakage AND the like, for the transformer overload, determining that the reasons for the transformer overload may include overcurrent AND voltage fluctuation, AND using a logical AND operator (AND) to indicate that the two reasons for the overcurrent AND the voltage fluctuation occur simultaneously, the overload is caused; for insulation breakage, possible causes include environmental damage and aging, which can be represented using logical OR Operators (OR).

As an alternative embodiment, determining a first probability of a plurality of power transmission and transformation devices failing according to a fault tree model includes: determining the occurrence probability of events corresponding to a plurality of basic nodes; determining the occurrence probability of a fault event to be analyzed corresponding to the top node according to the attribute of the connecting edges between the plurality of basic nodes and the top node; and determining a first probability of the faults of the power transmission and transformation equipment according to the probability of the faults to be analyzed.

Alternatively, the probability of occurrence of the event corresponding to the plurality of basic nodes may be initially estimated, specifically, may be estimated based on historical data, expert judgment and related documents, where the estimated probability represents the probability of occurrence of the fault event, for example, the overload probability of the transformer is 0.05, and the insulation breakage probability is 0.1. The probability of occurrence (first probability) of each fault event can then be calculated from the logical relationship between the nodes and the preliminary probability estimate by the fault tree model. The probability of occurrence of an event corresponding to the top node may be calculated stepwise up the branches of the tree starting from the basic event at the bottom of the fault tree model, the probability of the child node is transferred to the parent node according to logical relations (AND, OR), AND logical operations are performed using boolean algebra. Specifically, if the parent node represents AND logic, i.e., multiple child nodes occur simultaneously, then the probability of the parent node is equal to the product of the probabilities of all child nodes; if the parent node represents OR logic, i.e., at least one of the plurality of child nodes occurs, then the probability of the parent node is equal to 1 minus the product of the probabilities that all child nodes did not occur; the probability is transmitted from the bottom node to the upper node through gradual calculation until the probability is transmitted to the top node, and the probability of occurrence of an event corresponding to the top node represents the possibility of equipment failure. Finally, the risk degree of the equipment failure can be judged according to the calculated top-layer event probability.

The preliminary probability estimated value can be obtained based on historical data and expertise, the historical data of various power transmission and transformation equipment can be collected, and the frequency and the occurrence frequency of fault events can be calculated. For example, for a particular fault event, the number of times it occurred in the past year may be counted. The probability may be defined simply as the number of occurrences of the fault event divided by the total number of operations. For example, if a failure event occurs 20 times and 100 operations are performed in total, the probability of the failure event is 20/100=0.2.

As an alternative embodiment, determining the second probability of the plurality of power transmission and transformation devices failing according to the electrical parameters of the plurality of power transmission and transformation devices includes: and inputting the electrical parameters of the power transmission and transformation devices into a predetermined time sequence prediction model, and outputting a second probability by using the time sequence prediction model, wherein the time sequence prediction model is obtained by training a plurality of groups of training samples, and each group of samples in the plurality of groups of training samples comprises historical electrical parameters of the power transmission and transformation devices in a historical time period and the probability of faults in the historical time period.

Alternatively, a sensor may be installed for each power transmission and transformation device or device element to collect real-time data and based on the real-time sensor data, a time series prediction algorithm may be applied to predict device operating conditions, including potential faults and performance degradation. Wherein the data acquisition and preprocessing is to collect real-time data of the equipment elements and prepare them for subsequent predictive maintenance algorithms and fault tree analysis algorithms.

The following details how real-time data is collected for a plurality of power transmission and transformation devices: for each equipment element, selecting a proper sensor according to parameters (such as temperature, humidity, current, voltage and the like) to be monitored, wherein the selection of the sensor is based on the precision, reliability and adaptability, and the position and the installation mode of the sensor can not influence the normal operation of the equipment; a data acquisition system may be configured that can connect and read sensor data; setting the data acquisition frequency, namely how often to acquire data, which can be adjusted according to the importance, fault mode and data storage capacity of the equipment, and ensuring that the acquired data has consistent time stamps for subsequent time sequence analysis; monitoring the quality of sensor data, detecting whether abnormal values or data drift occur, and if the data are found to be abnormal, checking whether the sensor, a connecting line and the like have problems or not possibly; preprocessing the acquired data to ensure the accuracy and consistency of the data, wherein the preprocessing can comprise noise removal, missing value filling, data interpolation and abnormal value processing; the preprocessed data is stored in a database, data warehouse, or similar storage system for later analysis. For example: assuming that the temperature and current of a transformer are to be monitored, a temperature sensor and a current sensor are selected, which are suitable for the working environment and parameter range of the transformer, the temperature sensor is installed on the surface of the transformer, the current sensor measures the current of the transformer through electric connection, a data acquisition system is configured, temperature and current data are acquired every 15 minutes, an alarm mechanism is arranged in the data acquisition system so as to inform maintenance personnel in time when the temperature is abnormally high or the current is overloaded, and the acquired data are preprocessed, such as instantaneous noise is removed, and then stored in a database. Through the steps, accurate and reliable real-time data can be ensured to be acquired from the equipment element, and a basis is provided for subsequent predictive analysis.

The following details how a time series prediction algorithm can be applied to predict the device operating state: collecting experienced historical data, including sensor data such as equipment operating state, temperature, current, voltage and the like, and using the data as a training set for establishing a time sequence prediction model; preprocessing the collected historical data, including removing abnormal values, filling missing values, smoothing data and the like, so as to ensure the quality and stability of the data; performing time sequence analysis on the historical data, checking whether the data has trend, seasonality and periodicity, and selecting proper analysis model parameters according to analysis results; determining parameters of an analysis model, including an autoregressive order (p), a differential order (d) and a moving average order (q), according to the results of the time sequence analysis, and establishing the analysis model according to the selected parameters; training the established analysis model by using training data, and estimating parameter values of the model; using a part of data in the training data as a verification set, evaluating the prediction performance of the analysis model, and specifically, measuring the prediction error by using indexes such as Root Mean Square Error (RMSE); the real-time sensor data can be predicted by using a trained analysis model, and the predicted result is compared with an actual observed value to check the accuracy of the prediction; and judging whether the running state of the equipment is likely to have faults or performance degradation according to the prediction result of the analysis model, and if the prediction value accords with the abnormal condition in the historical data, taking maintenance into consideration. For example: assuming that the temperature of a transformer is to be predicted, historical data of the temperature of the past year can be collected and recorded once a day, smoothing and trending analysis is performed on the historical data, seasonal and trending are found on the data, parameters of an analysis model are selected to be (p=1, d=1, q=1) according to time sequence analysis, the first-order autoregressive, the first-order differential and the first-order moving average are represented, the analysis model is trained by using the data of the past 11 months, model evaluation is performed by using the data of the last month, and the temperature prediction is performed on the latest sensor data by using the trained analysis model. Through the above steps, the analysis model can be used to predict the operating state of the device based on historical data and real-time sensor data, identify potential faults and performance degradation, and thereby formulate a more efficient maintenance plan.

As an alternative embodiment, overhauling a plurality of power transmission and transformation devices according to the first probability and the second probability includes: determining the comprehensive probability of faults of a plurality of power transmission and transformation devices according to the first probability and the second probability; determining a scheme for overhauling a plurality of power transmission and transformation equipment according to the comprehensive probability; and overhauling the plurality of power transmission and transformation equipment according to the overhauling scheme of the plurality of power transmission and transformation equipment.

Alternatively, the first probability and the second probability may be obtained as a basis for planning the service. The probability of occurrence and the degree of influence of each fault event are determined according to the first probability of fault tree analysis, and a priority, such as high, medium and low priorities, or emergency, important, general and the like, is allocated to each fault event in combination with the second probability determined by the time sequence prediction algorithm, so that the fault events can be helped to distinguish which faults need emergency treatment and which can be treated later. The extent of impact of each fault event, i.e. the possible loss to the operation of the system after a fault has occurred, can also be assessed taking into account the economic cost, safety risk and reliability impact of the loss. For high priority fault events, corresponding maintenance strategies may be formulated, which may include immediate shutdown service, partial shutdown service, or online maintenance, etc. For example: it is assumed that in fault tree analysis, the probability of transformer overload is found to be high, and the influence is serious; the predictive maintenance algorithm also shows that the temperature of the transformer may exceed a safe range in the future week; setting the transformer overload event to a high priority in view of high probability and severe impact; making an overhaul plan, and determining to carry out shutdown overhaul in two days in the future so as to clean the transformer and ensure the normal operation of the transformer; through the steps, a reasonable overhaul plan can be formulated according to the fault probability and the influence degree, and fault events which possibly influence the stable operation and safety of equipment are preferentially processed.

Specifically, an optimal service time is determined based on the operating schedule, production schedule, and maintenance window of the equipment. Maintenance is selected to be performed for a suitable period of time in consideration of the load condition of the equipment and the influence on production. The proper overhaul mode can be selected according to the maintenance target and the equipment condition, and can be preventive overhaul, repairable overhaul or on-line maintenance. The most appropriate maintenance method is selected in consideration of the state and failure probability of the device. The maintenance personnel, tools, materials and equipment required can also be determined, with appropriate personnel and resources allocated according to the complexity of the service task and the skill required. Detailed maintenance steps and procedures can also be formulated, and maintenance personnel are guided to perform equipment inspection, cleaning, maintenance and testing step by step from the beginning of preparation work. The possible risks and potential safety hazards can be identified, corresponding risk management measures are formulated, the safety and reliability of the maintenance process are ensured, and accidents are avoided. The required spare parts and parts can be prepared according to the maintenance plan, the sufficiency of the spare parts is ensured, and the maintenance work is prevented from being blocked due to the lack of the spare parts. Before maintenance is started, necessary preliminary work such as equipment shutdown, disassembly work, equipment cleaning, etc. can be performed, ensuring smooth progress of the maintenance process. And executing maintenance work according to established maintenance steps, and recording key information in the maintenance process, such as operation records, maintenance conditions, used spare parts and the like. Recording in detail all information in the maintenance process, including problems, solutions, time and resources consumed, etc., will provide a useful reference for future maintenance decisions. After maintenance is completed, collecting feedback comments of maintenance personnel, evaluating the effect of maintenance execution, and improving and optimizing a maintenance plan and an execution process according to a feedback result.

For example: suppose that the service plan decides to shut down the transformer for service. Maintenance optimization decisions include selecting to service during low peak production periods, preparing the necessary maintenance personnel, tools, spare parts and equipment. The maintenance steps are refined, including cleaning insulation, inspecting cabling, testing current and voltage, etc. During maintenance, each step of service is recorded, an aged cable connection is found, and timely replacement is required. The maintenance personnel notice the temperature rise condition of the equipment in the maintenance process, and decide to replace the radiator in the maintenance process according to the result of the predictive maintenance algorithm. Through the steps, a refined maintenance decision can be made according to the formulated maintenance plan so as to ensure the smooth execution of the maintenance process and optimize the maintenance effect. This helps to improve the reliability and performance of the device.

The equipment overhaul method provided by the invention combines the predictive maintenance algorithm, the fault tree analysis algorithm and the topological structure (connection relation) of the power transmission and transformation equipment, and can bring various outstanding advantages in the overhaul process of the power transmission and transformation equipment:

(1) Comprehensive performance analysis: by combining the topological structure, the predictive maintenance algorithm and the fault tree analysis, the overall performance of the equipment can be comprehensively analyzed. The predictive maintenance algorithm provides possible future states of the equipment through time series analysis, fault tree analysis evaluates fault probability from the angle of logical relation, and topology structure simulation fuses the information to help determine proper maintenance time and strategy.

(2) Accurate maintenance prediction: the predictive maintenance algorithm can utilize historical data and real-time sensor data to accurately predict the future state of the device. By combining fault tree analysis, the occurrence probability of different fault events can be estimated more accurately, possible faults can be identified in advance, and targeted maintenance measures can be adopted.

(3) Optimizing maintenance decision: fault tree analysis and topology modeling help identify possible failure modes and impact paths. Predictive maintenance algorithms provide status information for devices. By combining the information, maintenance decisions can be optimized, a more targeted overhaul plan can be formulated, and the influence of maintenance on production is reduced to the greatest extent.

(4) System level considerations: fault tree analysis takes into account the logical relationships between different fault events, while topology modeling takes into account the physical connections of the devices. Such system level considerations enable a more comprehensive assessment of fault risk and maintenance requirements, thereby providing a more complete maintenance decision.

(5) Resource optimization and utilization: in connection with topology simulation, interactions between devices may be considered in the preparation of maintenance plans. This helps reasonable distribution manpower, material and time resource, avoids wasting of resources and repetitive work.

(6) Risk reduction: fault tree analysis enables the definition of the possible paths and potential causes of the fault occurrence. By combining with the predictive maintenance algorithm, potential faults can be predicted early. Through comprehensive analysis, a targeted maintenance strategy can be adopted, and risks brought by equipment faults are reduced.

The three methods are comprehensively utilized, so that more accurate, efficient and comprehensive maintenance planning of the power transmission and transformation equipment can be realized, and the stable operation and safety of the equipment are ensured to the greatest extent.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.

From the above description of the embodiments, it will be clear to a person skilled in the art that the method of equipment servicing according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.

According to an embodiment of the present invention, there is also provided an equipment inspection device for implementing the above equipment inspection method, and fig. 3 is a block diagram of the structure of the equipment inspection device provided according to the embodiment of the present invention, as shown in fig. 3, where the equipment inspection device includes: the equipment servicing device is described below as an acquisition module 31, a modeling module 32, a first determination module 33, a second determination module 34, and a service module 35.

The obtaining module 31 is configured to obtain a connection relationship between a plurality of power transmission and transformation devices, and electrical parameters of the plurality of power transmission and transformation devices.

The modeling module 32 is connected with the obtaining module 31, and is configured to construct a fault tree model of the multiple power transmission and transformation devices according to a connection relationship between the multiple power transmission and transformation devices, where the fault tree model is used to characterize a cause and a result of the multiple power transmission and transformation devices.

The first determining module 33 is connected to the modeling module 32, and is configured to determine a first probability that the plurality of power transmission and transformation devices fail according to the fault tree model.

The second determining module 34 is connected to the first determining module 33, and is configured to determine a second probability that the plurality of power transmission and transformation devices fail according to the electrical parameters of the plurality of power transmission and transformation devices.

And the overhaul module 35 is connected with the second determination module 34 and is used for overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

Here, it should be noted that the above-mentioned obtaining module 31, modeling module 32, first determining module 33, second determining module 34 and maintenance module 35 correspond to steps S202 to S210 in the embodiment, and a plurality of modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above-mentioned embodiments. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 10 provided in the embodiment.

Embodiments of the present invention may provide a computer device, optionally in this embodiment, the computer device may be located in at least one network device of a plurality of network devices of a computer network. The computer device includes a memory and a processor.

The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the equipment maintenance method and apparatus in the embodiments of the present invention, and the processor executes the software programs and modules stored in the memory, thereby executing various functional applications and data processing, that is, implementing the equipment maintenance method described above. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located relative to the processor, which may be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor may call the information and the application program stored in the memory through the transmission device to perform the following steps: acquiring connection relations among a plurality of power transmission and transformation devices and electrical parameters of the plurality of power transmission and transformation devices; constructing a fault tree model of a plurality of power transmission and transformation devices according to the connection relation among the plurality of power transmission and transformation devices, wherein the fault tree model is used for representing the reasons and results of the faults of the plurality of power transmission and transformation devices; determining a first probability of a plurality of power transmission and transformation devices to fail according to the fault tree model; determining a second probability of failure of the plurality of power transmission and transformation devices according to the electrical parameters of the plurality of power transmission and transformation devices; and overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

By adopting the embodiment of the invention, a scheme for equipment overhaul is provided. Acquiring connection relations among a plurality of power transmission and transformation devices and electrical parameters of the plurality of power transmission and transformation devices; constructing a fault tree model of a plurality of power transmission and transformation devices according to the connection relation among the plurality of power transmission and transformation devices, wherein the fault tree model is used for representing the reasons and results of the faults of the plurality of power transmission and transformation devices; determining a first probability of a plurality of power transmission and transformation devices to fail according to the fault tree model; determining a second probability of failure of the plurality of power transmission and transformation devices according to the electrical parameters of the plurality of power transmission and transformation devices; according to the first probability and the second probability, the plurality of power transmission and transformation equipment is overhauled, the purpose that the current electrical parameters of the plurality of power transmission and transformation equipment and the connection condition of the plurality of power transmission and transformation equipment are considered simultaneously to determine the risk of the current plurality of power transmission and transformation equipment to fail is achieved, so that the running state of the equipment is comprehensively known, the probability of the plurality of power transmission and transformation equipment to fail is accurately and timely predicted, the technical effect of overhauling the plurality of power transmission and transformation equipment is achieved according to the probability, and the technical problem that the equipment cannot be overhauled in time under the condition of higher failure risk due to the fact that the running state of the equipment cannot be comprehensively known in the related art is solved.

Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute on associated hardware, the program may be stored in a non-volatile storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.

Embodiments of the present invention also provide a nonvolatile storage medium. Alternatively, in the present embodiment, the above-described nonvolatile storage medium may be used to store the program code executed by the equipment servicing method provided in the above-described embodiment.

Alternatively, in this embodiment, the above-mentioned nonvolatile storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

Optionally, in the present embodiment, the non-volatile storage medium is arranged to store program code for performing the steps of: acquiring connection relations among a plurality of power transmission and transformation devices and electrical parameters of the plurality of power transmission and transformation devices; constructing a fault tree model of a plurality of power transmission and transformation devices according to the connection relation among the plurality of power transmission and transformation devices, wherein the fault tree model is used for representing the reasons and results of the faults of the plurality of power transmission and transformation devices; determining a first probability of a plurality of power transmission and transformation devices to fail according to the fault tree model; determining a second probability of failure of the plurality of power transmission and transformation devices according to the electrical parameters of the plurality of power transmission and transformation devices; and overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a non-volatile storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. A method of servicing an apparatus, comprising:

acquiring connection relations among a plurality of power transmission and transformation devices and electrical parameters of the plurality of power transmission and transformation devices;

constructing a fault tree model of the power transmission and transformation equipment according to the connection relation among the power transmission and transformation equipment, wherein the fault tree model is used for representing the reasons and results of the faults of the power transmission and transformation equipment;

determining a first probability of the plurality of power transmission and transformation devices to fail according to the fault tree model;

determining a second probability of failure of the plurality of power transmission and transformation equipment according to the electrical parameters of the plurality of power transmission and transformation equipment;

and overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

2. The method according to claim 1, wherein the constructing a fault tree model of the plurality of power transmission and transformation devices according to the connection relationship between the plurality of power transmission and transformation devices includes:

generating a top node of the fault tree model according to a predetermined fault event to be analyzed;

generating a plurality of basic nodes of the fault tree model according to a plurality of basic events respectively, wherein the plurality of basic events are events which lead to the occurrence of the fault event to be analyzed;

Determining the connection relation between the plurality of basic nodes and the top node according to the connection relation between the plurality of power transmission and transformation devices;

and constructing the fault tree model according to the connection relation among the plurality of basic nodes and the connection relation between the plurality of basic nodes and the top node.

3. The method according to claim 2, wherein the determining the connection relationship between the plurality of basic nodes and the top node according to the connection relationship between the plurality of power transmission and transformation devices, and constructing the fault tree model, includes:

determining father-son relations among the plurality of basic nodes according to the connection relations among the plurality of power transmission and transformation devices, wherein the father-son relations among the plurality of basic nodes represent event occurrence corresponding to the child nodes and cause event occurrence corresponding to the father nodes;

determining the connection relation among the plurality of basic nodes according to the parent-child relation among the plurality of basic nodes;

determining father-son relationships between the plurality of basic nodes and the top node according to the connection relationships among the plurality of power transmission and transformation devices, wherein the top node is connected with the basic node corresponding to the basic event which directly causes the fault to be analyzed;

And determining the connection relation between the plurality of basic nodes and the top node according to the parent-child relation between the plurality of basic nodes and the top node.

4. The method of claim 3, wherein constructing the fault tree model based on the connection relationships between the plurality of base nodes and the top node comprises:

determining the attribute of a connection edge between the plurality of basic nodes and the top node according to the connection relation among the plurality of power transmission and transformation devices, wherein the attribute of the connection edge represents that the occurrence of an event corresponding to a child node singly causes the occurrence of an event corresponding to a corresponding parent node, or the occurrence of the event corresponding to the plurality of child nodes commonly causes the occurrence of the event corresponding to the corresponding parent node;

and connecting the plurality of basic nodes with the top node according to the attribute of the connecting edges between the plurality of basic nodes and the top node, the connection relation between the plurality of basic nodes and the top node, and constructing the fault tree model.

5. The method of claim 4, wherein determining a first probability of the plurality of power transmission and transformation devices failing according to the fault tree model comprises:

Determining the occurrence probability of the events corresponding to the plurality of basic nodes;

determining the probability of occurrence of the fault event to be analyzed corresponding to the top node according to the attribute of the connecting edges between the plurality of basic nodes and the top node;

and determining a first probability of the faults of the power transmission and transformation equipment according to the probability of the faults to be analyzed.

6. The method of claim 1, wherein determining a second probability of the plurality of power transmission and transformation devices failing based on the electrical parameters of the plurality of power transmission and transformation devices comprises:

and inputting the electrical parameters of the power transmission and transformation devices into a predetermined time sequence prediction model, wherein the time sequence prediction model outputs the second probability, the time sequence prediction model is obtained through training of a plurality of groups of training samples, and each group of samples in the plurality of groups of training samples comprises historical electrical parameters of the power transmission and transformation devices in a historical time period and the probability of failure in the historical time period.

7. The method of any one of claims 1 to 6, wherein the overhauling the plurality of power transmission and transformation devices according to the first probability and the second probability comprises:

Determining the comprehensive probability of the faults of the power transmission and transformation equipment according to the first probability and the second probability;

determining a scheme for overhauling the plurality of power transmission and transformation equipment according to the comprehensive probability;

and overhauling the plurality of power transmission and transformation equipment according to the overhauling scheme of the plurality of power transmission and transformation equipment.

8. An equipment servicing device, comprising:

the acquisition module is used for acquiring the connection relation among the power transmission and transformation equipment and the electrical parameters of the power transmission and transformation equipment;

the modeling module is used for constructing fault tree models of the power transmission and transformation devices according to the connection relation among the power transmission and transformation devices, wherein the fault tree models are used for representing the reasons and results of the faults of the power transmission and transformation devices;

the first determining module is used for determining a first probability of the faults of the power transmission and transformation equipment according to the fault tree model;

a second determining module, configured to determine a second probability of failure of the plurality of power transmission and transformation devices according to the electrical parameters of the plurality of power transmission and transformation devices;

and the overhaul module is used for overhauling the plurality of power transmission and transformation equipment according to the first probability and the second probability.

9. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored program, wherein the program, when run, controls a device in which the non-volatile storage medium is located to perform the method of equipment servicing according to any of claims 1 to 7.

10. A computer device, comprising: a memory and a processor, wherein the memory is configured to store,

the memory stores a computer program;

the processor being configured to execute a computer program stored in the memory, the computer program when run causing the processor to perform the method of equipment servicing as claimed in any of claims 1 to 7.