High-voltage circuit breaker contact wear degree mutation prediction method based on big data analysis

Abstract

The invention relates to the field of circuit breakers, in particular to a high-voltage circuit breaker contact wear degree mutation prediction method based on big data analysis, and the method comprises the steps: calculating a high-voltage circuit breaker contact wear degree mutation possibility index; constructing a parameter time sequence based on the measured values; calculating an influence factor of the monitoring parameter at the current moment on the monitoring parameter at the next moment; predicting a monitoring parameter value of the next moment based on the parameter time sequence and the influence factor; calculating a contact wear degree sudden change possibility index prediction value at the next moment; and dynamically adjusting the maintenance strategy of the high-voltage circuit breaker according to the predicted value. The method has the advantages that the health state and the degradation trend of the contact can be dynamically reflected and predicted in real time by constructing the abrasion degree sudden change possibility index fused with the multi-dimensional dynamic parameters and establishing the time sequence prediction model; the limitation that a traditional method only considers single breaking wear or depends on a single parameter is overcome, and the real wear condition of the contact can be captured more comprehensively.

Description

Technical Field

[0001] This invention relates to the field of circuit breakers, and more particularly to a method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis. Background Technology

[0002] High-voltage circuit breakers are crucial control and protection devices in power systems, and their reliability directly affects the safe and stable operation of the power grid. The electrical life of a circuit breaker, especially the wear of its core component, the contacts, is a key indicator determining whether it needs maintenance or replacement. During repeated interruptions of fault currents or load currents, the contacts gradually wear down due to arc erosion. When the wear reaches a critical point, its performance undergoes a sudden change, potentially leading to serious faults such as interruption failure and insulation degradation, causing widespread power outages.

[0003] Currently, the wear assessment and electrical life prediction of high-voltage circuit breaker contacts mainly rely on the following two traditional methods: 1. Weighted cumulative method based on rules of thumb: The total wear is calculated by assigning a weighted ablation rate based on the breaking current to each opening operation of the circuit breaker. This method has significant limitations: First, only the wear during the opening process is considered, while the closing wear caused by electric arc and mechanical impact during the closing process is ignored, resulting in an incomplete wear estimation. Secondly, it is highly dependent on the accuracy of the arcing time measurement, but in actual engineering, accurate measurement of the arcing time is quite difficult and has a large error, which affects the accuracy of the final prediction result.

[0004] 2. Periodic maintenance and replacement based on fixed thresholds: This is currently the most common maintenance strategy: once a circuit breaker's operating time or number of interruptions reaches a preset fixed threshold, maintenance or direct contact replacement is scheduled. The drawback of this static maintenance method is that it doesn't consider the differences in the actual operating conditions of circuit breakers. For example, two circuit breakers with the same number of interruptions will have drastically different contact wear levels if one frequently interrupts large currents while the other mainly interrupts small currents. Using a "one-size-fits-all" fixed threshold can easily lead to two consequences: first, circuit breakers in poor operating conditions may not be maintained in time, resulting in "missed inspections" and potential safety hazards; second, circuit breakers in good operating conditions may be prematurely maintained or replaced, leading to "over-maintenance" and a waste of human and material resources. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis. It integrates multi-dimensional dynamic parameters such as circuit breaker contact temperature rise, contact resistance, vibration frequency, breaking current and number of breaking times to construct a quantitative indicator that can reflect the health status of the equipment in real time. This enables dynamic and accurate prediction of the wear and degradation trend of contacts, rather than simple static accumulation or experience judgment.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis includes: S1. Calculate the probability index of sudden changes in the wear degree of high-voltage circuit breaker contacts. and based on the index The composition of the index is used to obtain the set of relevant parameters required to influence the index. High-voltage circuit breaker contact wear degree sudden change probability index The calculation formula is: ①; In formula ①: for A fixed time interval For natural numbers, ; For the first At that moment, and For natural numbers, ; for Per-unit value of the temperature rise measurement of circuit breaker contacts at any given time; for The per-unit value of the contact resistance measurement of the circuit breaker contacts at any given time; for Per-unit value of the measured vibration frequency of the circuit breaker body at any given time; for Per-unit value of the circuit breaker breaking current measurement at any given time; for Per-unit value of the measured number of circuit breaker interruptions at any given time; S2. Measure each parameter in the dynamic monitoring parameter set and construct a parameter time series based on the measured values; S3. Calculate the influence factor of the current monitoring parameter on the monitoring parameter at the next time. ; S4. Predict the monitoring parameter values ​​for the next time step based on parameter time series and impact factors; S5. Using the predicted values ​​of the monitoring parameters at the next moment, calculate the predicted value of the contact wear degree sudden change probability index at the next moment. ; S6. Adjust the maintenance strategy of high-voltage circuit breakers dynamically based on the predicted values.

[0007] Constructing parameterized time series at fixed time intervals Within this framework, per-unit values ​​of various dynamic monitoring parameter sets are collected to form a multi-dimensional time series matrix, as shown in the following formula: ②.

[0008] Calculate the influence factor of the monitoring parameters at the current time on the monitoring parameters at the next time. The calculation formula is: ③; In formula ③: for Per-unit value of the temperature rise measurement of circuit breaker contacts at any given time; for The per-unit value of the contact resistance measurement of the circuit breaker contacts at any given time; for Per-unit value of the measured vibration frequency of the circuit breaker body at any given time; for Per-unit value of the circuit breaker breaking current measurement at any given time; for The per-unit value of the measured number of times the circuit breaker breaks.

[0009] The calculation formula is as follows: Based on the parameter time series and impact factors, the monitoring parameter values ​​at the next time step are predicted. ④; In formula ④: for Per-unit value of the temperature rise measurement of circuit breaker contacts at any given time; for The per-unit value of the contact resistance measurement of the circuit breaker contacts at any given time; for Per-unit value of the measured vibration frequency of the circuit breaker body at any given time; for Per-unit value of the circuit breaker breaking current measurement at any given time; for The per-unit value of the measured number of times the circuit breaker breaks.

[0010] Calculate the predicted value of the probability index of sudden change in contact wear at the next moment. The calculation formula is: ⑤.

[0011] The maintenance strategy for high-voltage circuit breakers is as follows: if This would extend the maintenance cycle of high-voltage circuit breakers; if If necessary, the maintenance will proceed according to the original schedule, and spare parts will be prepared. if The high-voltage circuit breaker should be shut down immediately for maintenance. in, for Time to The average historical value of the probability index of sudden changes in the wear degree of high voltage circuit breaker contacts, measured at any given time based on big data monitoring.

[0012] The set of dynamic monitoring parameters includes circuit breaker contact temperature rise, circuit breaker contact contact resistance, circuit breaker body vibration frequency, circuit breaker breaking current, and circuit breaker breaking number, and all parameters are calculated in per-unit value form.

[0013] A high-voltage circuit breaker contact wear abrupt change prediction system based on big data analysis includes a data acquisition module, a data processing module, a prediction module, and a decision output module. The data acquisition module is used to collect the measured values ​​of circuit breaker contact temperature rise, contact resistance, vibration frequency, breaking current and number of breaking times in real time and convert them into per-unit values. The data processing module is used to construct parameter time series and calculate impact factors; The prediction module is used to predict the monitoring parameters and the probability index of sudden changes in contact wear at the next moment based on the parameter time series and influencing factors. The decision output module is used to output corresponding maintenance strategies based on the predicted probability index of sudden changes in contact wear.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention abandons the traditional method that relies on fixed thresholds and simple empirical formulas. By constructing a "wear degree sudden change probability index" that integrates multi-dimensional dynamic parameters (contact temperature rise, contact resistance, vibration frequency, etc.) and establishing a time series prediction model, it can reflect and predict the health status and degradation trend of the contact in real time and dynamically, making the prediction results more accurate and reliable. 2. By comprehensively considering multiple factors such as circuit breaker breaking wear (e.g., breaking current, number of times) and condition characterization (e.g., temperature rise, contact resistance, vibration), a comprehensive evaluation index is constructed. This overcomes the limitations of traditional methods that only consider single breaking wear or rely on a single parameter. It can more comprehensively capture the true wear condition of the contacts, and in particular, it can sensitively reflect the potential risks of sudden changes in contact performance, significantly reducing the probability of missed detection and misjudgment. 3. The predicted index values ​​can provide quantitative guidance for maintenance decisions (such as extending the cycle, normal maintenance, or immediate shutdown), which transforms maintenance work from "periodic maintenance" to "condition-based maintenance". It can allocate maintenance resources "on demand" according to the actual health status of the equipment, which can effectively prevent power grid accidents caused by sudden failures and avoid unnecessary over-maintenance, thereby significantly improving the reliability of power grid operation and greatly reducing maintenance costs. 4. This invention not only assesses the current state but also predicts the wear risk at the next moment, achieving forward-looking early warning and leaving sufficient time window for scheduled maintenance. At the same time, the entire process (data acquisition → modeling → prediction → update) constitutes a dynamic closed-loop system that can continuously optimize itself as new data is continuously input, making the prediction model increasingly consistent with the actual operating characteristics of the circuit breaker over time, and continuously improving the prediction accuracy. 5. The monitoring parameters (such as temperature rise and vibration) used in this invention have been gradually popularized in smart substations and condition monitoring systems. They are easy to obtain, and the calculation models and processes are clear and explicit. They can be embedded into existing online monitoring systems through software algorithms or used to build independent prediction platforms. The implementation threshold is low, and they are easy to promote and apply to actual power grid operation and maintenance work. Attached Figure Description

[0015] Figure 1 This is a flowchart for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings, but it should be noted that the implementation of the present invention is not limited to the following embodiments.

[0017] The following embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments. Unless otherwise specified, the methods used in the following embodiments are conventional methods.

[0018] Example 1 The probability index of sudden changes in the wear degree of high-voltage circuit breaker contacts, based on big data monitoring, reflects the condition of high-voltage circuit breakers and enables dynamic adjustments to high-voltage circuit breaker monitoring. Accurately assessing and predicting the wear and degradation degree of high-voltage circuit breaker contacts not only helps the power grid to scientifically and rationally plan maintenance and repair schedules, but also avoids sudden faults, improving the operational efficiency and stability of the power grid. Especially for circuit breakers nearing their service life, implementing dynamic prediction methods can promptly identify key degradation factors such as contact erosion and insulation aging, providing data support for maintenance decisions, ensuring that circuit breakers can operate normally at critical moments, preventing large-scale power grid outages due to equipment failures, and improving the reliability of power grid operation.

[0019] See Figure 1 A method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis is analyzed, taking the LW10B-252 type high-voltage SF6 circuit breaker widely used in a 220kV substation of a certain city's power grid as an example. The method includes the following steps: Step S1: Obtain the probability index of sudden changes in the wear degree of high voltage circuit breaker contacts. and based on the index The composition of the parameters determines the set of associated dynamic monitoring parameters; High-voltage circuit breaker contact wear degree sudden change probability index Obtained through calculation formula ①: ①; In formula ①: for A fixed time interval For natural numbers, ; For the first At that moment, and For natural numbers, ; for The per-unit value of the circuit breaker contact temperature rise measurement is 40-55K during normal operation, with a reference value of less than or equal to 65K. If the actual measured temperature rise is 40K, then... ; for The per-unit value of the contact resistance measurement of the circuit breaker contacts is 18-30 μΩ during normal operation, with a reference value of less than or equal to 30 μΩ. For example, if the measured contact resistance is 19 μΩ, then... ; for The per-unit value of the circuit breaker body vibration frequency measurement is 10-50Hz during opening and closing operations, and less than 10Hz during steady-state operation. The reference value is less than or equal to 100Hz. For example, if the actual measured frequency is 50Hz, then... ; for The per-unit value of the circuit breaker breaking current measurement is given, with a base value of 50kA. For example, if the actual measured breaking current is 28kA, then... ; for The per-unit value of the circuit breaker's breaking count measurement is based on electrical life, with a base value of 20 times. If it has already broken 5 times, then... .

[0020] The set of dynamic monitoring parameters includes the per-unit values ​​of the above five parameters.

[0021] The probability index of sudden changes in the wear degree of high-voltage circuit breaker contacts, based on big data monitoring, reflects the characteristic relationship of five variables: circuit breaker contact temperature rise, circuit breaker contact contact resistance, circuit breaker body vibration frequency, circuit breaker breaking current, and circuit breaker breaking number. The faster the circuit breaker contact temperature rises, the greater the contact resistance, and the greater the sum of the two is compared to the sum of the parameters in the denominator. Therefore, the probability index of sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data monitoring is greater, indicating that the high-voltage circuit breaker contacts are severely eroded and need to be inspected in time, and preparations should be made to replace the circuit breaker.

[0022] Step S2: Measure each parameter in the dynamic monitoring parameter set and construct a parameter time series based on the measured values; Select A fixed time interval The per-unit value of the temperature rise of the circuit breaker contacts is obtained by measuring the temperature rise of the circuit breaker contacts. The per-unit value of the measured contact resistance of circuit breaker contacts. Per-unit value of the measured vibration frequency of the circuit breaker body Per-unit value of circuit breaker breaking current measurement The per-unit value of the circuit breaker breaking count measurement. .

[0023] Constructing parameterized time series at fixed time intervals Within this framework, per-unit values ​​of various dynamic monitoring parameter sets are collected to form a multi-dimensional time series matrix, as shown in the following formula: ②.

[0024] For example, select 6 times with a fixed time interval of 1 hour. Six sets of measurement values ​​were obtained from the circuit breaker contacts. Based on the parameter calculation method in formula ① and the influence factor calculation formula in formula ③, the per-unit values ​​of the six sets of parameter measurements were calculated, and a parameter time series was constructed to form a multidimensional time series matrix. The results are as follows: Based on the above data, the impact factor and time series matrix are obtained. .

[0025] Step S3: Calculate the influence factor of the monitoring parameters at the current time on the monitoring parameters at the next time. The calculation formula is: ③; In formula ③: for Per-unit value of the temperature rise measurement of circuit breaker contacts at any given time; for The per-unit value of the contact resistance measurement of the circuit breaker contacts at any given time; for Per-unit value of the measured vibration frequency of the circuit breaker body at any given time; for Per-unit value of the circuit breaker breaking current measurement at any given time; for The per-unit value of the measured number of times the circuit breaker breaks.

[0026] The impact factor is obtained based on the above data. .

[0027] Step S4: Predict the monitoring parameter values ​​for the next time step based on the parameter time series and impact factors. The calculation formula is as follows: ④; In formula ④: for Per-unit value of the temperature rise measurement of circuit breaker contacts at any given time; for The per-unit value of the contact resistance measurement of the circuit breaker contacts at any given time; for Per-unit value of the measured vibration frequency of the circuit breaker body at any given time; for Per-unit value of the circuit breaker breaking current measurement at any given time; for The per-unit value of the measured number of times the circuit breaker breaks.

[0028] Based on the above data, we obtain... Value at time: .

[0029] Step S5: Calculate the predicted value of the contact wear degree sudden change probability index at the next moment using the predicted value of the monitoring parameters at the next moment. The calculation formula is: ⑤.

[0030] Based on the above data, calculate .

[0031] In this embodiment, a real-time data acquisition module collects data on the temperature rise, contact resistance, body vibration frequency, breaking current, and number of breaking operations of the high-voltage circuit breaker contacts at fixed time intervals. A data processing module converts these measurements into per-unit values, constructs a parameter time series, forms a multi-dimensional time series matrix, and calculates the influence factor of the current monitoring parameter on the next monitoring parameter. The data processing and prediction modules then predict the monitoring parameter value for the next monitoring parameter based on the parameter time series and the influence factor. Finally, using the predicted monitoring parameter value for the next monitoring parameter, the predicted probability index of the contact wear degree mutation for the next monitoring parameter is calculated. Based on the predicted value, the circuit breaker... The decision output module dynamically adjusts the maintenance strategy of the high-voltage circuit breaker. If the predicted value of the mutation probability index is less than 0.8 times the average historical value of the contact wear mutation probability index, the maintenance cycle of the high-voltage circuit breaker can be extended. If the predicted value of the mutation probability index is less than or equal to 1.5 times the average historical value of the contact wear mutation probability index, and greater than or equal to 0.8 times the average historical value of the contact wear mutation probability index, the original maintenance plan can be executed and spare parts should be prepared. If the predicted value of the mutation probability index is greater than 1.5 times the average historical value of the contact wear mutation probability index, the high-voltage circuit breaker must be shut down for maintenance immediately.

[0032] Step S6: Based on the predicted values, dynamically adjust the maintenance strategy for high-voltage circuit breakers, including: if This would extend the maintenance cycle of high-voltage circuit breakers; if If necessary, the maintenance will proceed according to the original schedule, and spare parts will be prepared. if The high-voltage circuit breaker should be shut down immediately for maintenance. in, for Time to The average historical value of the probability index of sudden changes in the wear degree of high voltage circuit breaker contacts, measured at any given time based on big data monitoring.

[0033] A high-voltage circuit breaker contact wear abrupt change prediction system based on big data analysis includes a data acquisition module, a data processing module, a prediction module, and a decision output module. The data acquisition module is used to collect the measured values ​​of circuit breaker contact temperature rise, contact resistance, vibration frequency, breaking current and number of breaking times in real time and convert them into per-unit values. The data processing module is used to construct parameter time series and calculate impact factors; The prediction module is used to predict the monitoring parameters and the probability index of sudden changes in contact wear at the next moment based on the parameter time series and influencing factors. The decision output module is used to output corresponding maintenance strategies based on the predicted probability index of sudden changes in contact wear.

[0034] This invention abandons the traditional method that relies on fixed thresholds and simple empirical formulas. Instead, it constructs a "wear degree mutation probability index" that integrates multi-dimensional dynamic parameters (contact temperature rise, contact resistance, vibration frequency, etc.) and establishes a time-series prediction model. This allows for real-time and dynamic reflection and prediction of the health status and degradation trend of the contacts, making the prediction results more accurate and reliable. By comprehensively considering multiple factors such as circuit breaker breaking wear (e.g., breaking current, number of breaks) and condition characteristics (e.g., temperature rise, contact resistance, vibration), a comprehensive evaluation index is constructed. This overcomes the limitations of traditional methods that only consider single breaking wear or rely on a single parameter, and can more comprehensively capture the true wear condition of the contacts. In particular, it can sensitively reflect the potential risk of sudden changes in contact performance, significantly reducing the probability of missed detections and misjudgments. The predicted index value can provide quantitative guidance for maintenance decisions (e.g., extending the cycle, normal maintenance, or immediate shutdown), transforming maintenance work from "periodic maintenance" to "condition-based maintenance," allowing for adjustments based on design requirements. This invention allocates maintenance resources "on demand" based on the actual health status of the equipment, effectively preventing power grid accidents caused by sudden faults and avoiding unnecessary over-maintenance, thereby significantly improving the reliability of power grid operation while greatly reducing maintenance costs. This invention not only assesses the current state but also predicts wear risks at the next moment, achieving proactive early warning and providing ample time for planned maintenance. Simultaneously, the entire process (data acquisition → modeling → prediction → update) constitutes a dynamic closed-loop system that continuously optimizes itself with the input of new data, making the prediction model increasingly consistent with the actual operating characteristics of the circuit breaker over time, and continuously improving prediction accuracy. The monitoring parameters used in this invention (such as temperature rise and vibration) are gradually becoming widespread in smart substations and condition monitoring systems, are easy to obtain, and have clear and explicit calculation models and processes. They can be embedded into existing online monitoring systems through software algorithms or used to build independent prediction platforms, with low implementation barriers and easy application in actual power grid operation and maintenance.

Claims

1. A method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis, characterized in that, include: S1. Calculate the probability index of sudden changes in the wear degree of high-voltage circuit breaker contacts. and based on the index The composition of the index is used to obtain the set of relevant parameters required to influence the index. High-voltage circuit breaker contact wear degree sudden change probability index The calculation formula is: ①； In formula ①: for A fixed time interval For natural numbers, ; For the first At that moment, and For natural numbers, ; for Per-unit value of the temperature rise measurement of circuit breaker contacts at any given time; for The per-unit value of the contact resistance measurement of the circuit breaker contacts at any given time; for Per-unit value of the measured vibration frequency of the circuit breaker body at any given time; for Per-unit value of the circuit breaker breaking current measurement at any given time; for Per-unit value of the measured number of circuit breaker interruptions at any given time; S2. Measure each parameter in the dynamic monitoring parameter set and construct a parameter time series based on the measured values; S3. Calculate the influence factor of the current monitoring parameter on the monitoring parameter at the next time. ; S4. Predict the monitoring parameter values ​​for the next time step based on parameter time series and impact factors; S5. Using the predicted values ​​of the monitoring parameters at the next moment, calculate the predicted value of the contact wear degree sudden change probability index at the next moment. ; S6. Adjust the maintenance strategy of high-voltage circuit breakers dynamically based on the predicted values.

2. The method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis according to claim 1, characterized in that, The constructed parameter time series is at a fixed time interval. Within this framework, per-unit values ​​of various dynamic monitoring parameter sets are collected to form a multi-dimensional time series matrix, as shown in the following formula: ②。 3. The method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis according to claim 1, characterized in that, The calculation of the influence factor of the monitoring parameters at the current moment on the monitoring parameters at the next moment. The calculation formula is: ③； In formula ③: for Per-unit value of the temperature rise measurement of circuit breaker contacts at any given time; for The per-unit value of the contact resistance measurement of the circuit breaker contacts at any given time; for Per-unit value of the measured vibration frequency of the circuit breaker body at any given time; for Per-unit value of the circuit breaker breaking current measurement at any given time; for The per-unit value of the measured number of times the circuit breaker breaks.

4. The method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis according to claim 1, characterized in that, The formula for predicting the monitoring parameter values ​​for the next time step based on parameter time series and influence factors is as follows: ④； In formula ④: for Per-unit value of the temperature rise measurement of circuit breaker contacts at any given time; for The per-unit value of the contact resistance measurement of the circuit breaker contacts at any given time; for Per-unit value of the measured vibration frequency of the circuit breaker body at any given time; for Per-unit value of the circuit breaker breaking current measurement at any given time; for The per-unit value of the measured number of times the circuit breaker breaks.

5. The method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis according to claim 1, characterized in that, The predicted value of the probability index of sudden change in the degree of contact wear at the next moment is calculated. The calculation formula is: ⑤。 6. The method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis according to claim 1, characterized in that, The maintenance strategy for the high-voltage circuit breaker is as follows: if This would extend the maintenance cycle of high-voltage circuit breakers; if If necessary, the maintenance will proceed according to the original schedule, and spare parts will be prepared. if The high-voltage circuit breaker should be shut down immediately for maintenance. in, for Time to The average historical value of the probability index of sudden changes in the wear degree of high voltage circuit breaker contacts, measured at any given time based on big data monitoring.

7. The method for predicting sudden changes in the wear degree of high-voltage circuit breaker contacts based on big data analysis according to claim 1, characterized in that, The set of dynamic monitoring parameters includes circuit breaker contact temperature rise, circuit breaker contact contact resistance, circuit breaker body vibration frequency, circuit breaker breaking current, and circuit breaker breaking number, and all parameters are calculated in per-unit value form.

8. A high-voltage circuit breaker contact wear abrupt change prediction system based on big data analysis for implementing the method of any one of claims 1-7, characterized in that, It includes a data acquisition module, a data processing module, a prediction module, and a decision output module; The data acquisition module is used to collect the measured values ​​of circuit breaker contact temperature rise, contact resistance, vibration frequency, breaking current and number of breaking times in real time and convert them into per-unit values. The data processing module is used to construct parameter time series and calculate impact factors; The prediction module is used to predict the monitoring parameters and the probability index of sudden changes in contact wear at the next moment based on the parameter time series and influencing factors. The decision output module is used to output corresponding maintenance strategies based on the predicted probability index of sudden changes in contact wear.

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