A transformer health degree intelligent evaluation system based on fuzzy logic

Abstract

The application discloses a kind of transformer health degree intelligent evaluation system based on fuzzy logic, it is related to fuzzy logic reasoning field.The application obtains abnormal type combination and operation data value interval combination according to the test operation data kind with abnormal identification, and then generates several multimodal state combinations, sets initial decision evaluation value to each multimodal state combination, obtains result decision evaluation value by the initial decision evaluation value of each multimodal state combination for multiple rounds of iteration, starts to collect several real-time operation data from the target transformer put into use, whenever a health assessment time point judges that real-time operation data exists abnormal, according to the kind and the value of real-time operation data, multiple multimodal state combinations are retrieved, the multimodal state combination with the maximum result decision evaluation value is selected to update the residual life length of target transformer.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fuzzy logic reasoning, and particularly relates to a transformer health degree intelligent evaluation system based on fuzzy logic. BACKGROUND

[0002] The transformer is a crucial device in the power system, and the stability and reliability of its operating state directly affect the quality and safety of power supply. In actual operation, the transformer faces various complex working conditions and potential failure risks, and it is of great significance to accurately evaluate its operating state and take effective maintenance measures in a timely manner.

[0003] The existing transformer operating state monitoring and evaluation method has many limitations, and the data collection is not comprehensive and accurate. In the past, it may only rely on limited sensor collection of part of the operating data, and it is difficult to obtain multiple normal operating data intervals of the transformer, and it is impossible to comprehensively reflect the real operating state of the transformer under different working conditions, thereby leading to insufficient early warning ability of potential faults.

[0004] On the other hand, there is a lack of scientific and effective state analysis and decision mechanism. When facing the collected operating data, it is difficult to accurately judge the fault type corresponding to the data anomaly, and it is impossible to develop targeted operation and maintenance strategies according to different abnormal conditions, so that the maintenance work often has blindness and hysteresis, which not only increases the maintenance cost, but also may cause more serious safety accidents due to failure to handle the fault in time. In addition, there is a lack of effective method for evaluating the remaining life of the transformer, and the remaining life length cannot be dynamically updated according to the real-time operating state, and it is difficult to reasonably arrange the replacement and maintenance plan of the transformer, and therefore a transformer health degree intelligent evaluation system based on fuzzy logic is provided. SUMMARY

[0005] In order to solve the above technical problems, the purpose of the present application is to provide a transformer health degree intelligent evaluation system based on fuzzy logic.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme:

[0007] A transformer health degree intelligent evaluation system based on fuzzy logic, comprising a state data acquisition module, a multi-modal state analysis module and an operating state evaluation module.

[0008] The status data acquisition module is used to acquire multiple normal operating data ranges and rated service life of the test transformer via the Internet, and set multiple operation and maintenance actions. Based on the rated service life of the test transformer, a cyclic test event is set. During the execution of the cyclic test event, multiple test operation data are collected, and anomaly indicators are set for the test operation data through the normal operating data range. At the same time, various operation and maintenance actions are executed, and a test data set is generated when the cyclic test event ends.

[0009] The multimodal state analysis module is used to obtain the corresponding combination of abnormality types and the combination of running data value ranges according to the types of test running data with abnormality identifiers, and then generate several multimodal state combinations. An initial decision evaluation value is set for each multimodal state combination, and the final decision evaluation value is obtained by iterating through multiple rounds of the initial decision evaluation value of each multimodal state combination.

[0010] The operation status assessment module is used to collect several real-time operation data from the time the target transformer is put into use. Whenever a health assessment point determines that there is an abnormality in the real-time operation data, it retrieves multiple multimodal state combinations according to the type and value of the real-time operation data, selects the operation and maintenance action in the multimodal state combination with the largest result decision assessment value as the operation and maintenance decision of the target transformer, and updates the remaining lifespan of the target transformer according to the result decision assessment value.

[0011] Furthermore, the process of generating the test dataset includes:

[0012] Several types of sensors are installed on each test transformer. Cyclic test events are set according to the rated service life, and three operation and maintenance actions are set. The operation and maintenance actions include shutdown for maintenance, load reduction operation, and normal operation without operation.

[0013] Each test transformer is simultaneously subjected to several cyclic test events. During the execution of each cyclic test event, the status data acquisition module mobilizes all sensors to collect several test operation data of each test transformer, compares the normal operation data range of each test transformer with each test operation data, performs operation and maintenance actions based on the comparison results, and records the maintenance cost value P.

[0014] When it is determined that all cyclic test events of the test transformer have ended or the remaining cyclic test events have stopped, the status data acquisition module integrates various test operation data to generate the corresponding test data set for the test transformer.

[0015] Furthermore, the process of generating the modal state combination includes:

[0016] Three state dimensions are set up: exception type combination, running data value combination, and operation and maintenance action. Based on the time segment of the operation and maintenance action and the types of test running data with exception identifiers in the test data set, the corresponding exception type combination and running data value range combination are obtained.

[0017] Furthermore, based on the combination of various anomaly types, the combination of running data value ranges, and the operation and maintenance actions, several multimodal state combinations are set, and then test data sets that only appear once in each test data set are retrieved.

[0018] For multimodal state combinations with the same abnormality type combination and operating data value range combination, the average of the total duration of all test data sets of the corresponding test transformer under different operation and maintenance actions is obtained and denoted as the life extension value L.

[0019] Furthermore, a reward value R is set for different combinations of multimodal states. The formula for calculating the reward value R is: R = α * L - β * P, where α and β represent the lifetime weight coefficient and the cost weight coefficient, respectively.

[0020] Furthermore, the process of obtaining the outcome decision evaluation includes:

[0021] For multimodal state combinations with the same anomaly type and operation and maintenance actions, set equal initial decision evaluation values. Then, by using corresponding test data sets with the same anomaly type and operation and maintenance actions but different combinations of operation data value ranges, iterate the initial decision evaluation values ​​of each multimodal state combination multiple times, and obtain the result decision evaluation values ​​of each multimodal state combination based on the iteration.

[0022] The iterative formula for the initial decision evaluation value is:

[0023] K(A t B t C t )←K(A t B t C t )+μ[R t +γmaxK(A` t B` t C t )];

[0024] Where K(A) t B t C t ) represents the combination of exception types A t Combination B of running data value range t and operation and maintenance actions C tThe decision evaluation value after iteration, where μ and γ represent the learning parameter and discount parameter respectively, both ranging from (0, 1), R t K(A) represents t B t C t The corresponding reward value, maxK(A` t B` t C t ) Adopt operation and maintenance action C t Then, the maximum predicted decision evaluation value, A', is obtained from several test datasets. t and B` t Indicates the use of operation and maintenance action C. t The combination of subsequent exception types and the combination of running data value ranges.

[0025] Furthermore, based on the decision evaluation values ​​of each multimodal state combination, the lifetime wear value Loss of each multimodal state combination on the test transformer is quantified. The formula for calculating the lifetime wear value Loss is as follows: Where ζ represents the wear correction parameter.

[0026] Furthermore, the process of retrieving multiple multimodal state combinations based on the type and value of real-time operational data includes:

[0027] The operational status assessment module acquires several real-time operational data of the target transformer from the status data acquisition module starting from the time the target transformer is put into use.

[0028] Based on the rated service life, several health assessment time points with equal intervals and remaining life thresholds are set, and a state genetic node is set for each health assessment time point. Then, whenever a health assessment time point is reached, it is determined whether the various real-time operating data are within the normal operating data range of the target transformer.

[0029] If it is determined that all real-time operating data are within the corresponding normal operating data range, no operation will be performed;

[0030] If it is determined that there is real-time running data that is not within the corresponding normal operating data range, then extract the real-time running data segment that is not within the corresponding normal operating data range between the previous health assessment time point and the current health assessment time point, record it as an abnormal running data segment, and record the type of running data of the abnormal running data segment;

[0031] Then, the operation status assessment module retrieves multiple multimodal state combinations based on the numerical range of abnormal operation data segments and the type of operation data. It compares the result decision assessment values ​​corresponding to the retrieved multimodal state combinations and selects the operation and maintenance actions recorded in the multimodal state combination with the highest result decision assessment value as the operation and maintenance decision for the target transformer.

[0032] Furthermore, the process of updating the remaining lifespan of the target transformer based on the outcome-based decision evaluation includes:

[0033] The highest result decision evaluation value is substituted into the calculation formula of the life wear value Loss, and the remaining life duration of the target transformer is updated according to the calculation result of the life wear value Loss calculation formula.

[0034] Each time the remaining lifespan is updated, if the remaining lifespan of the target transformer is determined to be less than or equal to the remaining lifespan threshold, an insufficient lifespan warning is sent to the staff; otherwise, no action is taken.

[0035] Furthermore, the state genetic node associated with the health assessment time point records abnormal operating data fragments and corresponding operating data types, and passes the recording results to the next state genetic node. When abnormal operating data fragments appear at subsequent health assessment time points, the state genetic node determines whether the current abnormal operating data fragments and operating data types have already appeared based on the recorded abnormal operating data fragments and corresponding operating data types. If it is determined that they have appeared, the wear correction parameter ζ in the Loss of this calculated lifetime wear value is increased by Δw; otherwise, no operation is performed.

[0036] Repeat the above process of determining whether the target transformer has any abnormalities and updating the remaining lifespan until the remaining lifespan of the target transformer is less than or equal to the remaining lifespan threshold.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. This invention obtains corresponding abnormality type combinations and operating data value range combinations based on the test operation data types with abnormality identifiers, generates multiple multimodal state combinations, sets initial decision evaluation values, and performs multiple iterations to obtain result decision evaluation values, thereby improving the accuracy of analyzing the operating status of transformers and accurately determining the fault type and severity corresponding to different abnormal conditions.

[0039] 2. This invention sets health assessment time points and thresholds, and when anomalies are detected in real-time operating data at the health assessment time point, it retrieves multimodal state combinations based on the type and value of the real-time data, selecting the operation and maintenance action from the combination with the largest result decision assessment value as the operation and maintenance decision. This achieves real-time dynamic operation status assessment and decision-making. At the same time, it updates the remaining lifespan of the target transformer based on the result decision assessment value, making the assessment of the transformer's remaining lifespan more accurate and scientific. This helps to rationally arrange transformer replacement and maintenance plans, reduce operation and maintenance costs, and improve the safety and reliability of the power system. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0041] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0042] like Figure 1 As shown, a transformer health intelligent assessment system based on fuzzy logic includes a status data acquisition module, a multimodal status analysis module, and an operating status assessment module.

[0043] The status data acquisition module is used to acquire multiple normal operating data ranges and rated service life of the test transformer via the Internet, and set multiple operation and maintenance actions. Based on the rated service life of the test transformer, a cyclic test event is set. During the execution of the cyclic test event, multiple test operation data are collected, and anomaly indicators are set for the test operation data through the normal operating data range. At the same time, various operation and maintenance actions are executed, and a test data set is generated when the cyclic test event ends.

[0044] The multimodal state analysis module is used to obtain the corresponding combination of abnormality types and the combination of running data value ranges according to the types of test running data with abnormality identifiers, and then generate several multimodal state combinations. An initial decision evaluation value is set for each multimodal state combination, and the final decision evaluation value is obtained by iterating through multiple rounds of the initial decision evaluation value of each multimodal state combination.

[0045] The operation status assessment module is used to collect several real-time operation data from the time the target transformer is put into use, and to set several health assessment time points and health assessment thresholds. Whenever a health assessment time point determines that there is an anomaly in the real-time operation data, multiple multimodal state combinations are retrieved according to the type and value of the real-time operation data. The operation and maintenance action in the multimodal state combination with the largest result decision assessment value is selected as the operation and maintenance decision of the target transformer, and the remaining lifespan of the target transformer is updated according to the result decision assessment value.

[0046] Furthermore, the working principle of the present invention will be illustrated below through embodiments:

[0047] The status data acquisition module obtains multiple normal operating data ranges and rated service time of the test transformer through the Internet, and then sets up m test transformers of the same model, and installs several kinds of sensors on each test transformer at the same time, such as electrical parameter sensors, mechanical vibration sensors, thermodynamic sensors, etc., where m is a natural number greater than 50.

[0048] Set up cyclical test events based on the rated usage time, and set up three operation and maintenance actions, including shutdown for maintenance, load reduction operation, and normal operation without operation.

[0049] Each test transformer is instructed to perform n cyclic test events simultaneously, with a time interval of 60s between each adjacent cyclic test event, where n is a natural number greater than 0;

[0050] It should be noted that the total duration of n cyclic test events is equal to the rated usage time. If an abnormality occurs during the execution of a cyclic test event, causing the test transformer to malfunction, the execution of subsequent cyclic test events for the corresponding test transformer will be stopped.

[0051] During the execution of each cyclic test event, the status data acquisition module mobilizes all sensors to collect several test operation data of each test transformer, and compares the normal operation data range of each test transformer with each test operation data.

[0052] If it is determined that there is a data segment in the test operation data that is not within the corresponding normal operating data range, an abnormality flag is set for the corresponding data segment, and it is determined whether the corresponding test transformer can continue to operate. If it can continue to operate, it will randomly execute load reduction operation or normal operation without operation; otherwise, it will execute shutdown maintenance and record the maintenance cost value P until it is determined that the corresponding test transformer cannot be repaired, and then the remaining cycle test events will be stopped.

[0053] If the condition is met, and the data falls within the corresponding normal operating range, no action will be taken.

[0054] When it is determined that all cyclic test events of the test transformer have ended or the remaining cyclic test events have stopped, the status data acquisition module integrates various test operation data to generate the corresponding test data set for the test transformer.

[0055] Furthermore, the state data acquisition module sends the entire test data set to the multimodal state analysis module;

[0056] The multimodal state analysis module has an initial service life of rated usage time and is set with three state dimensions: abnormal type combination, running data value combination, and running and maintenance actions.

[0057] For each test dataset, based on the time segment of the operation and maintenance action and the types of test data with anomaly indicators in the test dataset, obtain the corresponding combination of anomaly types and the combination of numerical ranges of the test data.

[0058] Based on the combination of various anomaly types, the combination of running data value ranges, and the operation and maintenance actions, several multimodal state combinations are set, and then the test data set that only appears once in each test data set is retrieved.

[0059] For multimodal state combinations with the same abnormality type combination and operating data value range combination, the average of the total duration of all test data sets of the corresponding test transformer under different operation and maintenance actions is obtained and denoted as the life extension value L.

[0060] Furthermore, a reward value R is set for different combinations of multimodal states. The formula for calculating the reward value R is: R = α * L - β * P, where α and β represent the lifetime weight coefficient and the cost weight coefficient, respectively.

[0061] Furthermore, equal initial decision evaluation values ​​are set for multimodal state combinations with the same anomaly type and operation and maintenance actions. Then, by using corresponding test data sets with the same anomaly type and operation and maintenance actions but different combinations of operation data value ranges, the initial decision evaluation values ​​of each multimodal state combination are iterated multiple times, and the result decision evaluation values ​​of each multimodal state combination are obtained based on the iteration.

[0062] The iterative formula for the initial decision evaluation value is:

[0063] K(A t B t C t )←K(A t B t C t )+μ[R t +γmaxK(A` t B`t C t )];

[0064] Where K(A) t B t C t ) represents the combination of exception types A t Combination B of running data value range t and operation and maintenance actions C t The decision evaluation value after iteration, where μ and γ represent the learning parameter and discount parameter respectively, both ranging from (0, 1), R t K(A) represents t B t C t The corresponding reward value, maxK(A` t B` t C t ) Adopt operation and maintenance action C t Then, the maximum predicted decision evaluation value, A', is obtained from several test datasets. t and B` t Indicates the use of operation and maintenance action C. t The subsequent combination of exception types and the combination of running data value ranges;

[0065] Then, based on the results of each multimodal state combination, the evaluation value is determined, and the lifetime wear value Loss of each multimodal state combination on the test transformer is quantified. The formula for calculating the lifetime wear value Loss is as follows: Where ζ represents the wear correction parameter.

[0066] Furthermore, the operational status assessment module acquires several real-time operational data of the target transformer from the status data acquisition module starting from the time the target transformer is put into use. It should be noted that the model of the target transformer is the same as that of the test transformer.

[0067] Based on the rated service life, several health assessment time points with equal intervals and remaining life thresholds are set, and a state genetic node is set for each health assessment time point. Then, whenever a health assessment time point is reached, it is determined whether the various real-time operating data are within the normal operating data range of the target transformer.

[0068] If it is determined that all real-time operating data are within the corresponding normal operating data range, no operation will be performed;

[0069] If it is determined that there is real-time running data that is not within the corresponding normal operating data range, then extract the real-time running data segment that is not within the corresponding normal operating data range between the previous health assessment time point and the current health assessment time point, record it as an abnormal running data segment, and record the type of running data of the abnormal running data segment;

[0070] Then, the operation status assessment module retrieves multiple multimodal state combinations based on the numerical range of abnormal operation data segments and the type of operation data. It compares the result decision assessment values ​​corresponding to the retrieved multimodal state combinations and selects the operation and maintenance actions recorded in the multimodal state combination with the highest result decision assessment value as the operation and maintenance decision for the target transformer.

[0071] At the same time, the highest result decision evaluation value is substituted into the calculation formula of the life wear value Loss, and the remaining life duration of the target transformer is updated according to the calculation result of the life wear value Loss calculation formula.

[0072] Each time the remaining lifespan is updated, if the remaining lifespan of the target transformer is determined to be less than or equal to the remaining lifespan threshold, an insufficient lifespan warning is sent to the staff; otherwise, no action is taken.

[0073] Simultaneously, the state genetic node associated with the corresponding health assessment time point records the abnormal operation data fragments and the corresponding operation data types, and passes the recording results to the next state genetic node. When abnormal operation data fragments appear at subsequent health assessment time points, the state genetic node determines whether the current abnormal operation data fragments and operation data types have already appeared based on the recorded abnormal operation data fragments and the corresponding operation data types. If it is determined that they have appeared, the wear correction parameter ζ in the Loss of the calculated lifetime wear value is increased by Δw; otherwise, no operation is performed.

[0074] Repeat the above process of determining whether the target transformer has any abnormalities and updating the remaining lifespan until the remaining lifespan of the target transformer is less than or equal to the remaining lifespan threshold.

[0075] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A transformer health intelligent assessment system based on fuzzy logic, characterized in that, It includes a status data acquisition module, a multimodal status analysis module, and an operational status assessment module; The status data acquisition module is used to acquire multiple normal operating data ranges and rated service life of the test transformer via the Internet, and set multiple operation and maintenance actions. Based on the rated service life of the test transformer, a cyclic test event is set. During the execution of the cyclic test event, multiple test operation data are collected, and anomaly indicators are set for the test operation data through the normal operating data range. At the same time, various operation and maintenance actions are executed, and a test data set is generated when the cyclic test event ends. The multimodal state analysis module is used to obtain the corresponding combination of abnormality types and the combination of running data value ranges according to the types of test running data with abnormality identifiers, and then generate several multimodal state combinations. An initial decision evaluation value is set for each multimodal state combination, and the final decision evaluation value is obtained by iterating through multiple rounds of the initial decision evaluation value of each multimodal state combination. The operation status assessment module is used to collect several real-time operation data from the time the target transformer is put into use. Whenever a health assessment point determines that there is an abnormality in the real-time operation data, multiple multimodal state combinations are retrieved according to the type and value of the real-time operation data. The operation and maintenance action in the multimodal state combination with the largest result decision assessment value is selected as the operation and maintenance decision of the target transformer, and the remaining life of the target transformer is updated according to the result decision assessment value. The process of retrieving multiple multimodal state combinations based on the type and value of real-time operational data includes: Acquire several real-time operating data points of the target transformer starting from the time it is put into use; Based on the rated service life, several equally spaced health assessment time points and remaining life thresholds are set, and a state genetic node is set for each health assessment time point. Then, whenever a health assessment time point is reached, it is determined whether the various real-time operating data are within the normal operating data range of the target transformer. Based on the judgment results, extract the real-time running data segments that are not within the corresponding normal operating data range between the previous health assessment time point and the current health assessment time point, and record them as abnormal running data segments. Record the types of running data in the abnormal running data segments. Based on the numerical range of the abnormal operation data segments and the type of operation data, multiple multimodal state combinations are retrieved, the result decision evaluation values ​​corresponding to the retrieved multimodal state combinations are compared, and the operation and maintenance actions recorded in the multimodal state combination with the highest result decision evaluation value are selected as the operation and maintenance decision for the target transformer.

2. The intelligent transformer health assessment system based on fuzzy logic according to claim 1, characterized in that, The process of generating the test dataset includes: Several types of sensors are installed on each test transformer. Cyclic test events and operation and maintenance actions are set according to the rated service time. The operation and maintenance actions include shutdown for maintenance, load reduction operation, and normal operation without operation. Each test transformer is simultaneously subjected to several cyclic test events. During the execution of each cyclic test event, several test operation data of the test transformer are collected by sensors. The normal operation data range of the test transformer is compared with each test operation data. Based on the comparison results, operation and maintenance actions are performed, and the maintenance cost value P is recorded. When it is determined that all cyclic test events of the test transformer have ended or the remaining cyclic test events have been stopped, the test data of each test operation are integrated to generate the corresponding test data set of the test transformer.

3. The intelligent transformer health assessment system based on fuzzy logic according to claim 2, characterized in that, The process of generating the modal state combination includes: Three state dimensions are set up: combination of exception types, combination of running data values, and operation and maintenance actions. Based on the types of test running data with exception markers in the test data set in each time segment when the operation and maintenance actions are performed, the combination of exception types and combination of running data value ranges are obtained.

4. The intelligent transformer health assessment system based on fuzzy logic according to claim 3, characterized in that, Based on the combination of various anomaly types, the combination of running data value ranges, and the operation and maintenance actions, several multimodal state combinations are set, and then the test data set that only appears once in each test data set is retrieved. For multimodal state combinations with the same abnormality type combination and operating data value range combination, the average of the total duration of all test data sets of the corresponding test transformer under different operation and maintenance actions is obtained and denoted as the life extension value L. A reward value R is set for different combinations of multimodal states. The reward value R is calculated by the formula: R = α * L - β * P, where α and β represent the lifetime weight coefficient and the cost weight coefficient, respectively.

5. The intelligent transformer health assessment system based on fuzzy logic according to claim 4, characterized in that, The process of obtaining the outcome decision evaluation includes: For multimodal state combinations with the same anomaly type and operation and maintenance actions, set equal initial decision evaluation values. Then, based on the corresponding test data sets with the same anomaly type and operation and maintenance actions but different combinations of operation data value ranges, iterate the initial decision evaluation values ​​of each multimodal state combination multiple times, and obtain the result decision evaluation values ​​of each multimodal state combination based on the iteration.

6. The intelligent transformer health assessment system based on fuzzy logic according to claim 5, characterized in that, Based on the decision evaluation values ​​of each multimodal state combination, the lifetime wear value of each multimodal state combination on the test transformer is obtained.

7. The intelligent transformer health assessment system based on fuzzy logic according to claim 6, characterized in that, The process of updating the remaining lifespan of the target transformer based on the outcome-based decision assessment includes: The lifespan wear value is obtained based on the highest result decision evaluation value. The remaining lifespan of the target transformer is updated based on the lifespan wear value. Whenever the remaining lifespan is updated, if it is determined that the remaining lifespan of the target transformer is less than or equal to the remaining lifespan threshold, an insufficient lifespan prompt is sent to the staff; otherwise, no action is taken.

8. The intelligent transformer health assessment system based on fuzzy logic according to claim 7, characterized in that, The state genetic node associated with the health assessment time point records abnormal operating data fragments and corresponding operating data types, and passes the recording results to the next state genetic node. When abnormal operating data fragments appear at subsequent health assessment time points, the state genetic node determines whether the current abnormal operating data fragment and operating data type have already appeared based on the recorded abnormal operating data fragments and corresponding operating data types. If it is determined that they have appeared, the process of obtaining the lifetime wear value is corrected; otherwise, no operation is performed.

Images