A method and system for evaluating reliability of a power distribution switch spring operating mechanism

Abstract

The application discloses a kind of methods and systems for evaluating the reliability of power distribution switch spring operating mechanism, belong to power distribution switch reliability analysis technical field.The method of the present application comprises: the correlation of characteristic quantity and the failure of mechanism, the failure of component part and the failure of part is carried out, and the association relationship is determined;Collecting power distribution switch spring operating mechanism stroke, current and fracture data and calculating the value of characteristic quantity;Determine the failure membership degree of component part;According to the failure membership degree of component part, determine the failure membership degree of component part, and evaluate the reliability of power distribution switch spring operating mechanism according to the failure membership degree of component part and mechanism predicted life.The present application has strong adaptability, and can be flexibly applied in equipment reliability evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution switch reliability analysis, and more particularly, to a method and system for evaluating the reliability of a power distribution switch spring operating mechanism. BACKGROUND

[0002] The power distribution switch is one of the important components of the power system, and the reliability research of the power distribution switch is crucial for both the power grid company and the equipment manufacturer. The mechanism is an important and high-failure-rate device part in the power distribution switch. The reliability analysis of the mechanism has high practical value. For the power grid company, the mechanism reliability test research helps to control the quality of the grid-connected equipment, develop supplier evaluation, assist in switch operation and maintenance, and spare parts management. For the equipment manufacturer, the reliability test research can provide a reference for optimizing product design and process, and equipment component selection.

[0003] Currently, the main methods for conducting power distribution switch mechanism reliability research include: reliability research on specific component mechanisms and materials, functional and parametric testing before factory delivery or network connection, statistical analysis of in-network equipment, and fault monitoring and state evaluation of in-network equipment.

[0004] In the prior art, most of the reliability analysis is carried out on local components or parts, or even on materials, and there is a lack of systematic research ideas and methods from the overall mechanism. If the mechanism reliability short board cannot be effectively detected and identified, and targeted measures are not taken, the overall equipment reliability cannot be improved.

[0005] Currently, many reliability researches are carried out on in-network equipment, such as online monitoring and diagnosis of faults. However, the amount of data generated by online monitoring is limited, and there is a risk of difficult identification of faults and equipment "sick" network connection. SUMMARY

[0006] To solve the above problems, the present application provides a method for evaluating the reliability of a power distribution switch spring operating mechanism before factory delivery or network connection, comprising:

[0007] Determine the fault type and characteristic quantity of the power distribution switch spring operating mechanism, hierarchically decompose the fault type into mechanism failure, component part failure and part failure, correlate the characteristic quantity with the mechanism failure, component part failure and part failure, and determine the correlation relationship;

[0008] Collect the stroke, current sensing and fracture signal data of the power distribution switch spring operating mechanism and calculate the value of the characteristic quantity;

[0009] According to the correlation, the feature quantity is used to apply fuzzy reasoning to calculate the fault membership degree of the component part, and the fault membership degree of the component part is determined; and according to the feature quantity value and the number of switch actions, a prediction model is trained;

[0010] According to the fault membership degree of the component part and the prediction model, the reliability of the spring operating mechanism of the power distribution switch is determined.

[0011] Optionally, the fault type is hierarchically decomposed, and the decomposition is performed according to the physical structure and linkage characteristics of the spring operating mechanism of the power distribution switch.

[0012] Optionally, the feature quantity is a parameter reflecting the state of the spring operating mechanism of the power distribution switch, including: opening characteristic quantity and closing characteristic quantity.

[0013] Optionally, the correlation is established according to the component action linkage relationship and the correlation degree of the spring operating mechanism of the power distribution switch.

[0014] Optionally, the fault membership degree of the component part is determined, including:

[0015] A fuzzy reasoning subsystem of the fault is established for the opening characteristic quantity and the closing characteristic quantity, the values of the opening characteristic quantity and the closing characteristic quantity are subjected to fuzzy processing according to the limit value and the membership function of the feature quantity, the fuzzy processing result is obtained, the fuzzy reasoning subsystem is used to reason the processing result, the reasoning result is obtained, and the processing result is processed in a weighted processing manner to determine the fault membership degree of the component part.

[0016] Optionally, the reliability of the spring operating mechanism of the power distribution switch is determined, including:

[0017] After each opening and closing round, the component fault membership degree is used as the basis to calculate the assembly fault membership degree, and after a plurality of tests, the average values of the assembly fault membership degrees in different stages are calculated, the average value comparison graph of each assembly in different stages is drawn according to the average values, and the assembly layer weak point affecting the reliability of the mechanism is qualitatively determined; the assembly fault membership degree is used as the basis to further calculate the mechanism fault membership degree by weighting; the prediction model is trained based on the historical feature quantity and the switch action counter, the feature quantity limit value is used as the prediction input, and the switch life is predicted; finally, the switch life, the mechanism fault membership degree and the assembly fault weak point are used as the result of the reliability evaluation of the spring operating mechanism of the power distribution switch.

[0018] The application further provides a system for evaluating the reliability of the spring operating mechanism of the power distribution switch, including:

[0019] An initial unit determines a fault type and a characteristic quantity of a spring operating mechanism of a power distribution switch, hierarchically decomposes the fault type into a mechanism fault, a component part fault and a part fault, associates the characteristic quantity with the mechanism fault, the component part fault and the part fault, and determines an association relationship;

[0020] A collection unit collects stroke, current sensing and fracture signal data of the spring operating mechanism of the power distribution switch.

[0021] A calculation unit determines a part fault membership degree using a calculation value and a limit value of the characteristic quantity, determines a component part fault membership degree according to the part fault membership degree, and trains a prediction model according to the characteristic quantity value and a switch action number.

[0022] An evaluation unit determines a reliability of the spring operating mechanism of the power distribution switch according to the component part fault membership degree and the prediction model.

[0023] Optionally, the fault type is hierarchically decomposed according to a physical structure and a linkage feature of the spring operating mechanism of the power distribution switch.

[0024] Optionally, the characteristic quantity is a parameter reflecting a device state of the spring operating mechanism of the power distribution switch, and includes a tripping characteristic quantity and a closing characteristic quantity.

[0025] Optionally, the association relationship is established according to a part action linkage relationship and an association degree of the spring operating mechanism of the power distribution switch.

[0026] Optionally, the determination of the part fault membership degree includes:

[0027] A fuzzy reasoning subsystem of the tripping characteristic quantity and the closing characteristic quantity is established, the values of the tripping characteristic quantity and the closing characteristic quantity are subjected to fuzzy processing according to a limit value and a membership function of the characteristic quantity, a fuzzy processing result is obtained, the fuzzy processing result is subjected to reasoning using the fuzzy reasoning subsystem to obtain a reasoning result, the processing result is subjected to processing in a weighted processing manner, and the part fault membership degree is determined.

[0028] Optionally, the determination of the reliability of the spring operating mechanism of the power distribution switch includes:

[0029] After each opening and closing cycle, the component failure membership is calculated based on the failure component membership, and after a number of tests, the average value of the component failure membership in different stages is calculated, and according to the average value, the average value comparison chart of each component in different stages is drawn, and the weak point of the component layer affecting the reliability of the mechanism is qualitatively determined; based on the component failure membership, the mechanism failure membership is further calculated by weighting; based on the historical characteristic quantity and the switch action counter, the prediction model is trained, the characteristic quantity limit value is taken as the prediction input, and the switch life is predicted; finally, the switch life, the mechanism failure membership and the component failure weak point are taken as the results of the reliability evaluation of the spring operating mechanism of the distribution switch.

[0030] The present application has strong adaptability and can be flexibly applied to equipment reliability evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 The flowchart of the method of the present application is shown in the figure;

[0032] Figure 2 The logic diagram of the embodiment of the method of the present application is shown in the figure;

[0033] Figure 3 The flowchart of the embodiment of the method of the present application is shown in the figure;

[0034] Figure 4 The hierarchical decomposition diagram of the failure type of the spring operating mechanism of the distribution switch in the embodiment of the method of the present application is shown in the figure;

[0035] Figure 5.a The failure calculation architecture and the associated characteristic quantity diagram of the power transmission chain in the embodiment of the method of the present application are shown in the figure;

[0036] Figure 5.b The failure calculation architecture and the associated characteristic quantity diagram of the operating mechanism in the embodiment of the method of the present application are shown in the figure;

[0037] Figure 5.c The failure calculation architecture and the associated characteristic quantity diagram of the energy storage device in the embodiment of the method of the present application are shown in the figure;

[0038] Figure 5.d The failure calculation architecture and the associated characteristic quantity diagram of the spring in the embodiment of the method of the present application are shown in the figure;

[0039] Figure 5.e The failure associated characteristic quantity diagram of the auxiliary switch in the embodiment of the method of the present application is shown in the figure;

[0040] Figure 5.f The failure associated characteristic quantity diagram of the buffer in the embodiment of the method of the present application is shown in the figure;

[0041] Figure 6 The transmission device jamming (closing) fuzzy expert subsystem diagram of the power transmission chain failure in the embodiment of the method of the present application is shown in the figure;

[0042] Figure 7 A flow chart of the fuzzy inference algorithm in the embodiment of the method of the present application;

[0043] Figure 8 An example graph of comparison of membership degrees of component faults in the embodiment of the method of the present application;

[0044] Figure 9 A structure diagram of the system of the present application. DETAILED DESCRIPTION

[0045] Reference will now be made to the drawings to describe the exemplary embodiments of the present application in detail. The present application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and fully convey the scope of the application to those skilled in the art. Like reference numerals refer to like elements throughout the specification. It will be understood that when an element is referred to as being "on" another element, it can be directly on the element or intervening elements can also be present. In addition, terms such as first and second are used herein when claimed elements have particular properties. These properties are not used to limit the claims to the first or second elements but are used merely as labels to make identification of the elements easier for the reader.

[0046] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0047] The present application proposes a method for evaluating reliability of a spring operating mechanism of a distribution switch, as shown in Figure 1 The method comprises the following steps:

[0048] Determining a fault type and a characteristic quantity of the spring operating mechanism of the distribution switch, hierarchically decomposing the fault type into a fault of the mechanism, a fault of a component part, and a fault of a part, correlating the characteristic quantity with the fault of the mechanism, the fault of the component part, and the fault of the part, and determining a correlation relationship;

[0049] Collecting detection data of the spring operating mechanism of the distribution switch, and calculating a value of the characteristic quantity;

[0050] According to the correlation relationship, applying fuzzy inference to calculate a membership degree of the fault of the part by using the characteristic quantity, and determining the membership degree of the fault of the part; training a prediction model according to the value of the characteristic quantity and a number of switch actions;

[0051] According to the membership degree of the fault of the part and the prediction model, determining the reliability of the spring operating mechanism of the distribution switch.

[0052] The fault type is hierarchically decomposed according to a physical structure and a linkage feature of the spring operating mechanism of the distribution switch.

[0053] Wherein, the characteristic quantity is a parameter reflecting the device state of the power distribution switch spring operating mechanism, including: opening characteristic quantity and closing characteristic quantity.

[0054] Wherein, the correlation relationship is established according to the component action linkage relationship and correlation degree of the power distribution switch spring operating mechanism.

[0055] Wherein, the fault membership of the component part is determined, including:

[0056] A fuzzy reasoning subsystem of the fault of the opening characteristic quantity and the closing characteristic quantity is established, the values of the opening characteristic quantity and the closing characteristic quantity are subjected to fuzzy processing according to the limit value and the membership function of the characteristic quantity, the fuzzy processing result is obtained, the fuzzy reasoning subsystem is used to reason the processing result, the reasoning result is obtained, and the processing result is processed in a weighted processing manner to determine the fault membership of the component part.

[0057] Wherein, the reliability of the power distribution switch spring operating mechanism is determined, including:

[0058] After each opening and closing round is completed, the component fault membership is calculated based on the fault component membership, and after a plurality of tests, the average values of the component fault memberships in different stages are calculated, and according to the average values, an average value comparison graph of each component in different stages is drawn to qualitatively determine the component layer weak point affecting the reliability of the mechanism; the component fault membership is further used as the basis for weighted calculation of the mechanism fault membership; the historical characteristic quantity and the switch action counter are used as the data basis to train a prediction model, the characteristic quantity limit value is used as the prediction input, and the switch life is predicted; finally, the switch life, the mechanism fault membership and the component fault weak point are used as the results of the reliability evaluation of the power distribution switch spring operating mechanism.

[0059] The application will be further described below in combination with embodiments:

[0060] The embodiment process is shown in Figure 2 and Figure 3 ;

[0061] Step 1 mechanism fault type hierarchical decomposition and fault type and characteristic quantity correlation, including:

[0062] Step 1 mechanism fault type hierarchical decomposition

[0063] According to the physical structure, association and linkage relationship of the spring operating mechanism type power distribution switch, the power distribution switch mechanism fault types are divided into three layers, the first layer is the whole mechanism, the second layer is each component part, and the third layer is each component part. The second layer component part is divided into six parts, namely power transmission chain fault, operating mechanism fault, energy storage device fault, spring fault, auxiliary switch fault and buffer jam. The third layer component part is divided into 16 types, including 5 types of power transmission chain fault, 4 types of operating mechanism fault, 3 types of energy storage device fault, 2 types of spring fault, 1 type of auxiliary switch fault and 1 type of buffer jam. The specific hierarchical architecture is shown in Figure 4 .

[0064] Step 2: Summarize the monitorable feature quantity

[0065] For the spring operating mechanism, by monitoring the energy storage motor current, split / close coil current, contact displacement stroke, fracture and other signals, 20 feature quantities with correlation are summarized, and the specific names of each feature quantity are shown in Table 1 (the data given in Table 1 is only a reference example, not the accurate data of a certain switch).

[0066] Table 1

[0067]

[0068] Step 3: Establish the association relationship between fault type and feature quantity

[0069] According to the specific component characteristics of the mechanism, the action linkage relationship of the mechanism components, and the correlation degree, the association relationship between the mechanism component fault type and the feature quantity is established as shown in the content of Figure 5.

[0070] Step 2: Data acquisition and feature quantity calculation

[0071] Step 3: Component fault membership degree and component fault membership degree calculation, including the following:

[0072] Step 1: Component fault membership degree calculation decomposition and integration

[0073] Some component opening feature quantities and closing feature parameters are independently established as fuzzy reasoning systems for fault membership degree calculation, and finally the calculation results are weighted and summed in equal weight. As shown in Figure 5.a The transmission device jam, insulation pull rod fracture, transmission main shaft deformation and contact wear are divided into two subsystems of closing feature parameters and opening feature parameters for fuzzy calculation.

[0074] For the same fault component, if different fault components correspond to the same associated feature quantity and have the same associated characteristics, they are merged. For example, Figure 5.bThe closing tripping stuck and closing electromagnet fault, opening tripping stuck and opening electromagnet fault, Figure 5.c The chain breakage and energy storage mechanism slip can be merged into a group of fuzzy reasoning subsystems respectively.

[0075] Step 2 Component fault membership degree calculation

[0076] First, according to the limit value of the correlation characteristic quantity, the input characteristic quantity is fuzzy processed by using the membership function, and the output is fuzzy processed according to the fault degree. Different fuzzy subsystems create different fuzzy controllers for reasoning analysis. The fuzzy rules of the fuzzy controller are established according to the expert experience knowledge; Figure 6 The transmission device sticking (closing) fuzzy expert subsystem is established, and the other fuzzy expert subsystems are established with reference to this subsystem, Figure 7 The fuzzy reasoning general algorithm flow chart is used to apply the mamdani fuzzy reasoning to execute the reasoning algorithm of the established rules, and the centroid method is used to de-fuzzify to obtain the fuzzy reasoning result y * ;

[0077] The centroid method calculation is as follows

[0078]

[0079] y * , which represents the degree of fault, where C represents the output fuzzy set, n is the number of output fuzzy sets, and μc(y) is the membership function of the output fuzzy set.

[0080] The above is the calculation of single fuzzy reasoning. For the decomposition type component fault that independently establishes fuzzy subsystems for closing and opening characteristic parameters, the fault membership degree is calculated by the following formula:

[0081]

[0082] Where, is the component fault membership degree, is the calculation result of the fuzzy subsystem established by the closing and opening characteristic parameters of the component membership degree respectively.

[0083] Step 3 Component fault membership degree calculation

[0084] On the basis of component fault membership degree calculation, the component fault membership degree is calculated by weighting method, such as Figure 5.a , assuming that the fault membership degrees of transmission device sticking, insulation pull rod breakage, transmission main shaft deformation, contact pressure spring performance degradation, and contact wear are , respectively, Where i = 1, 2,..., 5, and The corresponding weights are , respectively, Where i = 1, 2,..., 5, and Power transmission chain failure is D, then:

[0085]

[0086] Where It can be valued by subjective experience method, or obtained by statistical history failure rate of each component of power transmission chain failure type.

[0087] The calculation method of operating mechanism failure, energy storage device failure, spring failure is the same as that of power transmission chain failure. The corresponding component failure type of auxiliary switch failure and buffer jamming failure is only one example, and the component failure membership value is the calculation result of component failure membership.

[0088] If the overall reliability of the mechanism needs to be analyzed, it needs to be further calculated by weighting method based on the component failure membership.

[0089] Step 4 Component reliability analysis and mechanism life prediction

[0090] Step 1 Average of component membership classification

[0091] Assuming that n opening and closing round tests are continuously carried out, the failure memberships of power transmission chain failure, operating mechanism failure, energy storage device failure, spring failure, auxiliary switch failure and buffer jamming are respectively D i ,C i ,E i ,S i ,F i ,H i ,1≤i≤n, each group of failure memberships is divided into m groups (it is recommended that m is a natural number between 3-6) according to the order, if n can be divided by m, then the number of each type is equal, if it cannot be divided by m, then the first m-1 groups take n / m integer according to the order of failure membership, and the last m group takes the remaining order of failure membership value. After grouping, the average value of each group of failure memberships is calculated, taking power transmission chain failure as an example, the calculation formula is as follows:

[0092]

[0093]

[0094] Where, n / m is rounded, and the grouping average value of the remaining component membership is calculated in the same way as the above formula.

[0095] Step 2 Draw comparison chart and analyze results

[0096] The average value of the fault membership degree of each component is calculated, and the average value is further compared and analyzed to determine the relatively weak mechanism components, such as Figure 8 A comparative example of dividing the fault membership degree into four groups for 1000 detection times is shown. The change trend of the average value of the fault membership degree and the size comparison of the fault membership degrees of different components in the same test stage can be intuitively shown by the diagram.

[0097] Step 3: Mechanism life prediction

[0098] As shown in Figure 5.a Before starting the test, the number of switch action times is recorded as J L , and JL is automatically increased by 1 every time a closing and opening action round is detected, and the feature quantity calculation result of the round is corresponded to the action sequence data. Finally, the action sequence J +i is formed, where 1≤i≤n, and n is the number of actions.

[0099] The samples are trained by using a supervised learning algorithm (in view of the advantages of support vector machine in small sample prediction and generalization ability, the application recommends applying the method for life prediction), the training input is the calculated feature quantity, the output is the corresponding action sequence, the prediction input is the limit value of the feature quantity in Table 1, and the output is the mechanism life.

[0100] The application further provides a system 200 for evaluating the reliability of a power distribution switch spring operating mechanism, as shown in Figure 9 , comprising:

[0101] An initial unit 201 determines the fault type and feature quantity of the power distribution switch spring operating mechanism, hierarchically decomposes the fault type, decomposes the fault of the mechanism into the fault of the component part and the fault of the part, associates the feature quantity with the fault of the part, and determines the association relationship;

[0102] A collection unit 202 collects the stroke, current sensing and fracture signal data of the power distribution switch spring operating mechanism;

[0103] A calculation unit 203 calculates the value of the feature quantity according to the collected data, determines the part fault membership degree by using the calculated value and the limit value of the feature quantity, determines the component part fault membership degree according to the part fault membership degree, trains a prediction model according to the feature quantity value and the switch action times, and an evaluation unit 204 determines the reliability of the power distribution switch spring operating mechanism according to the component part fault membership degree and the prediction model.

[0104] The fault type is hierarchically decomposed according to the physical structure and linkage characteristics of the power distribution switch spring operating mechanism.

[0105] The characteristic quantity is a parameter reflecting the device state of the power distribution switch spring operating mechanism, and includes a tripping characteristic quantity and a closing characteristic quantity.

[0106] The correlation relationship is established according to the component action linkage relationship and the correlation degree of the power distribution switch spring operating mechanism.

[0107] The fault membership degree of the component part is determined, including:

[0108] The fuzzy inference subsystem of the fault of the tripping characteristic quantity and the closing characteristic quantity is established, the values of the tripping characteristic quantity and the closing characteristic quantity are subjected to fuzzy processing according to the limit value and the membership function of the characteristic quantity, the fuzzy processing result is obtained, the fuzzy inference subsystem is used to infer the processing result, the inference result is obtained, and the processing result is processed in a weighted processing manner to determine the fault membership degree of the component part.

[0109] The reliability of the power distribution switch spring operating mechanism is determined, including:

[0110] After each tripping and closing round is completed, the component fault membership degree is calculated based on the fault component membership degree, the average value of the component fault membership degree is calculated in stages after a plurality of tests, the average value comparison graph of each component in different stages is drawn according to the average value, and the component layer weak point affecting the reliability of the mechanism is qualitatively determined; the component fault membership degree is further used to calculate the mechanism fault membership degree by weighting; the prediction model is trained based on the historical characteristic quantity and the switch action counter as data, the switch life is predicted based on the characteristic quantity limit value as the prediction input; and finally, the switch life, the mechanism fault membership degree and the component fault weak point are used as the reliability evaluation result of the power distribution switch spring operating mechanism.

[0111] The application has strong adaptability and can be flexibly applied to device reliability evaluation.

[0112] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer usable program codes. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming language Java and interpreted scripting language JavaScript.

[0113] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0114] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0115] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0116] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims intend to cover all such modifications and variations as fall within the true spirit and scope of the application.

[0117] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for evaluating the reliability of a spring-operated mechanism of a power distribution switch, the method comprising: Determine the fault type and characteristic quantity of the spring operating mechanism of the power distribution switch, perform hierarchical decomposition of the fault type, decompose the mechanism fault into component fault and part fault, and correlate the characteristic quantity with the part fault to determine the correlation relationship. Data on the stroke, current sensing, and break signal of the spring operating mechanism of the power distribution switch are collected, and the values ​​of characteristic quantities are calculated. Based on the correlation, the fault membership degree of the component is determined by using the calculated values ​​and limits of the feature quantities; based on the fault membership degree of the component, the fault membership degree of the assembly is determined; and a prediction model is trained based on the feature quantity values ​​and the number of switching actions. The reliability of the spring operating mechanism of the power distribution switch is determined based on the fault membership degree and prediction model of the component. The fault membership calculation for the component part includes: First, based on the limits of the associated features, the input features are fuzzified using membership functions. The output is then fuzzified according to the fault degree. Different fuzzy controllers are created for different fuzzy subsystems for inference analysis. Fuzzy rules for the fuzzy controllers are established based on expert experience. The Mamdani fuzzy inference algorithm is applied to execute the rule-building algorithm. After defuzzification using the centroid method, the fuzzy inference result y is obtained. * ; The centroid method calculation is shown in the following formula: y * This represents the degree of the fault, where y i This represents the output fuzzy set, where n is the number of output fuzzy sets, and μc(y) is the membership function of the output fuzzy set. The above calculations are for a single fuzzy inference. For component faults where the opening and closing characteristic parameters are independently established as fuzzy subsystems, the fault membership degree is calculated using the following formula: in, For component failure membership, The calculation results of the fuzzy subsystem established by the membership degree closing and opening characteristic parameters of this component are respectively; Determining the reliability of the distribution switch spring operating mechanism includes: After each opening and closing cycle, the component fault membership degree is calculated based on the component fault membership degree. After several consecutive tests, the average value of the fault membership degree of each component is calculated in stages. Based on the average value, a comparison chart of the average values ​​of each component in different stages is drawn to qualitatively analyze the weak points of the components in each stage. Based on the membership degree of component failures, the membership degree of mechanism failures is calculated by weighting. Using historical feature quantities and switch action counters as data, a prediction model is trained, and the feature quantity limits are used as prediction inputs to predict switch life. Finally, the switch life, mechanism failure membership degree, and component failure weak points are used as the results of the reliability assessment of the distribution switch spring operating mechanism.

2. The method according to claim 1, wherein the hierarchical decomposition of fault types is performed based on the physical structure and linkage characteristics of the spring operating mechanism of the power distribution switch.

3. The method according to claim 1, wherein the characteristic quantity is a parameter reflecting the equipment state of the spring operating mechanism of the power distribution switch, including: Opening characteristic quantity, closing characteristic quantity.

4. The method according to claim 1, wherein the correlation is established based on the linkage relationship and degree of correlation of the components of the power distribution switch spring operating mechanism.

5. The method according to claim 1, wherein determining the fault membership degree of the component part includes: Fuzzy reasoning is applied to perform fuzzy processing on the feature quantities based on the limit values ​​and membership functions of the feature quantities, obtain the fuzzy processing results, use the fuzzy reasoning subsystem to perform reasoning, defuzzify the reasoning results, and determine the fault membership degree of the component.

6. A system for evaluating the reliability of a spring-operated mechanism of a power distribution switch, the system comprising: The initial unit determines the fault type and characteristic quantity of the spring operating mechanism of the power distribution switch. The fault type is decomposed hierarchically into mechanism faults, component faults, and part faults. The characteristic quantity is correlated with the part fault to determine the correlation relationship. The data acquisition unit collects data on the stroke of the spring operating mechanism of the power distribution switch, current sensing, and break signal. The calculation unit uses the calculated values ​​and limits of characteristic quantities to determine the fault membership degree of the component part, and determines the fault membership degree of the assembly part based on the fault membership degree of the component part. A prediction model is trained based on feature values ​​and the number of switching actions. The evaluation unit determines the reliability of the spring operating mechanism of the power distribution switch based on the fault membership degree of the component and the prediction model. The fault membership calculation for the component part includes: First, based on the limits of the associated features, the input features are fuzzified using membership functions. The output is then fuzzified according to the fault degree. Different fuzzy controllers are created for different fuzzy subsystems for inference analysis. Fuzzy rules for the fuzzy controllers are established based on expert experience. The Mamdani fuzzy inference algorithm is applied to execute the rule-building algorithm. After defuzzification using the centroid method, the fuzzy inference result y is obtained. * ; The centroid method calculation is shown in the following formula: y * This represents the degree of the fault, where y i This represents the output fuzzy set, where n is the number of output fuzzy sets, and μc(y) is the membership function of the output fuzzy set. The above calculations are for a single fuzzy inference. For component faults where the opening and closing characteristic parameters are independently established as fuzzy subsystems, the fault membership degree is calculated using the following formula: in, For component failure membership, The calculation results of the fuzzy subsystem established by the membership degree closing and opening characteristic parameters of this component are respectively; Determining the reliability of the distribution switch spring operating mechanism includes: After each opening and closing cycle, the component fault membership degree is calculated based on the membership degree of the faulty component. After several consecutive tests, the average value of the fault membership degree of each component is calculated in stages. Based on the average value, a comparison chart of the average values ​​of each component in different stages is drawn to qualitatively analyze the weak points of the component in each stage. Based on the membership degree of component faults, the membership degree of mechanism faults is calculated by weighting. Using historical feature quantities and switch action counters as data, a prediction model is trained, and the switch life is predicted with feature quantity limits as prediction input. Finally, the switch life, mechanism fault membership degree, and component fault weak points are used as the results of the reliability assessment of the distribution switch spring operating mechanism.

7. The system according to claim 6, wherein the hierarchical decomposition of fault types is performed based on the physical structure and linkage characteristics of the power distribution switch spring operating mechanism.

8. The system according to claim 6, wherein the characteristic quantity is a parameter reflecting the equipment state of the spring operating mechanism of the power distribution switch, including: Opening characteristic quantity and closing characteristic quantity.

9. The system according to claim 6, wherein the association relationship is established based on the linkage relationship and degree of association of the components of the power distribution switch spring operating mechanism.

10. The system according to claim 6, wherein determining the fault membership of the component part includes: A fuzzy inference subsystem for faults is established for the tripping and closing characteristic quantities. The values ​​of the tripping and closing characteristic quantities are fuzzy processed according to the limit values ​​and membership functions of the characteristic quantities to obtain the fuzzy processing results. The fuzzy inference subsystem is used to infer the fuzzy processing results to obtain the inference results. The processing results are then processed in a weighted manner to determine the fault membership degree of the component.

Images