A method and system for predicting electrical joint failures

Abstract

This invention discloses a method and system for predicting electrical joint faults, belonging to the field of electrical joint fault prediction technology. Through microstructural degradation analysis, it obtains microstructural information such as initial strain and strain response rate, and analyzes its changes using a strain accumulation model and a nanostructure dynamic response model. Subsequently, it performs electrical performance response evaluation, collecting parameters such as initial contact resistance to quantify the impact of microstructural changes on electrical performance. Based on this, a fault prediction model is constructed. Potential fault risks are assessed by comparing the fault occurrence probability with a preset risk threshold. When a risk occurs, a maintenance priority quantification value is calculated based on the fault occurrence probability, compared with a preset threshold range, and a maintenance decision is generated. Priorities are assigned and maintenance is notified. Through multi-dimensional analysis and precise model construction, the accuracy of electrical joint fault prediction is significantly improved, facilitating the early detection of potential fault risks and making maintenance work more targeted.

Description

Technical Field

[0001] This invention relates to the field of electrical joint fault prediction technology, and specifically to an electrical joint fault prediction method and system. Background Technology

[0002] With the rapid development of flexible electronics technology, flexible electrical connectors have been widely used in fields such as intelligent manufacturing, wearable devices, and aerospace. However, due to the susceptibility of the microstructure of flexible materials to stress, environmental factors, and other influences, electrical performance deteriorates, which in turn affects the stability and safety of the system. Therefore, this paper proposes an electrical connector fault prediction method that combines microstructure degradation, electrical performance response, and intelligent prediction to improve prediction accuracy and achieve precise maintenance.

[0003] Existing electrical joint fault prediction methods and systems have at least the following technical problems: 1. Traditional electrical joint fault prediction methods often rely on a single electrical performance parameter, such as the change in resistance value, to predict faults. This method ignores the degradation changes in microstructure and cannot reveal the subtle damage to electrical joints during long-term use. This leads to inaccurate and untimely detection of early faults, thus missing the best time for maintenance and replacement.

[0004] 2. Existing methods for predicting electrical joint failures lack quantitative analysis for specific flexible electrical joints. They often use general models for prediction, which makes it impossible to make accurate judgments based on the actual working conditions and environmental characteristics of each joint when applied to different types of electrical joints. Therefore, in practical applications, they cannot provide personalized and targeted prediction results, thus affecting the rationality and accuracy of maintenance decisions.

[0005] 3. Traditional electrical joint fault prediction methods often lack a systematic dynamic adjustment mechanism in fault risk assessment and maintenance decision optimization. Although a few traditional electrical joint fault prediction methods have fault prediction capabilities, they often do not fully consider factors such as the actual operating time of the equipment, environmental conditions, and the importance of the equipment. This leads to a lack of scientific basis when setting maintenance priorities, resulting in the neglect of potential faults or premature maintenance of equipment, causing unnecessary waste of resources. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for predicting electrical joint faults, which solves the problems existing in the background art.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for predicting electrical joint faults, comprising the following steps:

[0008] Step 1: Microstructure degradation analysis: Obtain the microstructure information of the specified flexible electrical connector during use, and then analyze the changes in the microstructure of the specified flexible electrical connector during use.

[0009] Step 2: Electrical performance response assessment: Based on the structural changes of the specified flexible electrical connector during use, and by collecting the corresponding electrical performance parameters of the specified flexible electrical connector, the impact of microstructural changes on the corresponding electrical performance of the specified flexible electrical connector is quantified.

[0010] Step 3: Fault Risk Prediction: Based on the impact of microstructural changes on the electrical performance of the specified flexible electrical connector, a fault prediction model for the specified flexible electrical connector during use is constructed to assess whether there are potential fault risks for the specified flexible electrical connector.

[0011] Step 4: Real-time decision optimization: When a specified flexible electrical joint has potential failure risks, the maintenance priority of the corresponding potential failure risks of the specified flexible electrical joint is calculated based on the output results of the failure prediction model, and then the maintenance decision of the corresponding potential failure risks of the specified flexible electrical joint is generated.

[0012] In a second aspect, the present invention provides an electrical joint fault prediction system, comprising: a microstructure degradation analysis module, used to acquire microstructure information of a specified flexible electrical joint during use, and then analyze the microstructure changes of the specified flexible electrical joint during use.

[0013] The electrical performance response evaluation module is used to quantify the impact of microstructural changes on the electrical performance of a specified flexible electrical connector by collecting the corresponding electrical performance parameters based on the structural changes of the connector during use.

[0014] The fault risk prediction module is used to construct a fault prediction model for a specified flexible electrical connector during use based on the impact of microstructural changes on the corresponding electrical performance, and then assess whether the specified flexible electrical connector has potential fault risks.

[0015] The real-time decision optimization module is used to calculate the maintenance priority of the potential fault risk corresponding to the specified flexible electrical joint based on the output of the fault prediction model when the specified flexible electrical joint has potential fault risk, and then generate the maintenance decision corresponding to the potential fault risk of the specified flexible electrical joint.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] 1. The present invention provides an electrical joint failure prediction method and system. In the process of microstructure degradation analysis, by acquiring the microstructure information of a specified flexible electrical joint during use, such as initial strain, strain response rate, and nanostructure response sensitivity, it is beneficial to evaluate the microstructure changes of the joint in actual use in detail. The microstructure information obtained by microscopy equipment and dynamic tensile test helps to accurately capture the microstructure changes of the joint during use, which is beneficial to identify potential material degradation problems in advance, avoid joint failure caused by material fatigue, and thus improve the service life and safety of electrical joints.

[0018] 2. In the electrical performance response evaluation process, this invention collects electrical performance parameters of a specified flexible electrical joint, such as initial contact resistance and its variation coefficient, to quantify the impact of microstructure changes on electrical performance. Combined with information on microstructure changes, the dynamic changes in electrical performance are accurately tracked, which helps to improve the accuracy of electrical joint fault prediction. Since electrical performance is a key indicator of fault occurrence, the evaluation method provided by this invention helps to identify the trend of declining electrical performance earlier, thereby providing a strong basis for subsequent fault prediction and risk assessment.

[0019] 3. In the process of fault risk prediction, the present invention constructs a fault prediction model based on microstructure changes and electrical performance, which is beneficial to assess the potential fault risks of the joint during use. The prediction model helps to output the probability of fault occurrence in a timely manner, providing data support for joint maintenance decisions, which is beneficial to accurately assess the probability of fault occurrence and helps to provide early risk warning, reduce the losses caused by sudden faults, and improve the reliability and operational safety of electrical joints.

[0020] 4. In the real-time decision optimization process of this invention, when there is a potential fault risk in the joint, the electrical joint fault prediction system calculates the maintenance priority through the fault prediction model and generates the corresponding maintenance decision. This realizes intelligent analysis and optimization decision-making of fault risks, effectively reduces unnecessary maintenance costs, and ensures that the joint can be maintained in a timely manner when a potential fault occurs. This is conducive to achieving accurate maintenance scheduling, avoiding the waste of resources caused by blind maintenance, and ensuring that key equipment receives timely maintenance when it is most needed, thereby improving the operating efficiency of the entire system. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the steps in the electrical connector fault prediction method of the present invention.

[0023] Figure 2 This is a schematic diagram of the electrical connector fault prediction system of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1

[0026] Please see Figure 1 As shown, this embodiment provides a method for predicting electrical joint faults. The method includes: Step 1, microstructure degradation analysis: obtaining the microstructure information of a specified flexible electrical joint during use, and then analyzing the changes in the microstructure of the specified flexible electrical joint during use.

[0027] In a specific embodiment, the process of obtaining the microstructure information of the specified flexible electrical connector during use is as follows: The microstructure information includes initial strain, strain response rate, initial response intensity, nanostructure response sensitivity, and intrinsic vibration frequency. A control sample of the same model as the specified flexible electrical connector is selected. The original length and deformation of the flexible material surface of the control sample corresponding to the specified flexible electrical connector are observed using a microscope. The ratio of the deformation to the original length is used to obtain the initial strain. The strain response rate experimental data are measured through a dynamic tensile test. The material strain of the control sample is corresponding to each experimental time point of the strain response rate experimental data. The change curve of material strain versus experimental time points is then fitted to obtain the strain response rate.

[0028] The structural information of the control sample corresponding to the specified flexible electrical connector is determined by microscopy when it is not subjected to external force, thereby obtaining the initial reaction intensity. The reaction of the control sample corresponding to the specified flexible electrical connector under external force is then fitted to obtain the nanostructure reaction sensitivity. At the nanoscale, the resonance frequency of the flexible material corresponding to the control sample is measured by applying external excitation to the control sample corresponding to the specified flexible electrical connector, thereby obtaining the intrinsic vibration frequency.

[0029] It should be noted that microscope equipment includes, but is not limited to, optical microscopes, scanning electron microscopes, and atomic force microscopes. External excitations such as micro-vibrations and pulse excitations, and being subjected to external force, refer to, for example, using a clamp to stretch a control sample, causing a change in the length of the control sample. This stretching force is an external force.

[0030] In a specific embodiment, the analysis of the microstructure changes of the specified flexible electrical connector during use is carried out as follows: By establishing a strain accumulation model and a nanostructure dynamic response model, the microstructure changes of the specified flexible electrical connector during use are evaluated. The expression corresponding to the strain accumulation model is: α(t)=α0*(1-exp(-v*T)), where α(t) represents the strain of the flexible material of the specified flexible electrical connector at time t, α0 and v represent the initial strain and strain response rate of the specified flexible electrical connector during use, T represents the usage time of the specified flexible electrical connector, and the time between the installation of the specified flexible electrical connector and time t is T. exp(-v*T) is an exponential function.

[0031] The expression corresponding to the dynamic response model of nanostructures is:

[0032] β(t) = β0*(1-λ*cos(f*T)), where β(t) represents the nanostructure reaction intensity of the flexible material corresponding to the specified flexible electrical connector at time t, β0, λ and f represent the initial reaction intensity, nanostructure reaction sensitivity and intrinsic vibration frequency of the specified flexible electrical connector during use, and cos(f*T) represents the cosine function, with the value range of cos(f*T) being [-1, 1].

[0033] It should be noted that the calculation process of the exponential function exp(-v*T) is as follows: when the result of (v*T) equals 5, then exp(-5) = e -5 =2.71828 -5 .

[0034] Step 2: Electrical performance response assessment: Based on the structural changes of the specified flexible electrical connector during use, and by collecting the corresponding electrical performance parameters of the specified flexible electrical connector, the impact of microstructural changes on the corresponding electrical performance of the specified flexible electrical connector is quantified.

[0035] In a specific embodiment, the process of collecting the electrical performance parameters corresponding to the specified flexible electrical connector is as follows: The electrical performance parameters include the initial contact resistance, the electrical performance variation coefficient affecting the contact resistance, and the influence coefficient of the electrical performance variation on the contact resistance. Under no external load, the initial contact resistance of the flexible material corresponding to the specified flexible electrical connector is determined by standard electrical performance testing, and the electrical performance variation coefficient affecting the contact resistance and the influence coefficient of the electrical performance variation on the contact resistance are obtained by fitting experimental data.

[0036] It should be noted that in the experiment, by changing a certain electrical performance parameter of the flexible electrical connector, such as environmental factors like temperature and humidity, the electrical properties of the material, such as conductivity, were affected. Simultaneously, the change in contact resistance was measured. Assuming temperature was the variable, the ambient temperature of the flexible electrical connector was changed according to a set step size, and the contact resistance values ​​were measured at different temperatures. Then, mathematical methods were used to fit the experimental data to obtain a function that describes the relationship between changes in electrical performance and changes in contact resistance. For example, temperature; the coefficient of this function is the coefficient of electrical performance change affecting contact resistance. In the experiment, it was found that when… For every 1°C increase in temperature, the contact resistance increases by an average of 0.05 ohms. This 0.05 ohm is the coefficient of electrical performance variation affecting the contact resistance. For example, in a series of experiments, it was found that when the material's conductivity decreased by 10%, the contact resistance increased by 20%. Through calculation and fitting, a coefficient of 2 was obtained. This 2 represents the influence coefficient of electrical performance variation on contact resistance, indicating that for every 1 unit change in conductivity, the contact resistance changes by 2 units. This is a simplified example; the specific experimental process is the same as the experiment on the coefficient of electrical performance variation affecting contact resistance, and will not be elaborated further here.

[0037] In a specific embodiment, the influence of the quantified microstructure changes on the electrical performance of a specified flexible electrical connector is achieved through the following process: Based on the strain of the flexible material and the nanostructure response intensity of the specified flexible electrical connector at time t, the following formula is used for calculation: The change in electrical performance Δχ(t) of the flexible material corresponding to a specified flexible electrical joint at time t is obtained, which is the change in electrical performance Δχ(t) of the specified flexible electrical joint after a service time T. and These are the weighting factors for the dependent variable and the weighting factor for the reaction intensity of the nanostructure, respectively.

[0038] By specifying the dynamic resistance evolution formula corresponding to the flexible electrical connector:

[0039] δ(t)=δ0+μ*(1+κ*Δχ(t)),to obtain the resistance δ(t) of the specified flexible electrical joint at time t, where δ0, μ and κ represent the initial contact resistance of the specified flexible electrical joint, the electrical performance variation coefficient affecting the contact resistance and the influence coefficient of the electrical performance variation on the contact resistance, respectively.

[0040] It should be noted that, and The values ​​of are all greater than 0 and less than 1. and Based on established and mature technical methods in related fields, this technical system, through the analysis of a large amount of experimental data and considering various factors in the actual application scenarios of electrical connectors, determined the... and The specific values ​​will not be elaborated on here.

[0041] In this embodiment, during the electrical performance response evaluation process, electrical performance parameters of a specified flexible electrical joint, such as initial contact resistance and its variation coefficient, are collected to quantify the impact of microstructural changes on electrical performance. Combined with information on microstructural changes, the dynamic changes in electrical performance are accurately tracked, which helps to improve the accuracy of electrical joint fault prediction. Since electrical performance is a key indicator of fault occurrence, the evaluation method provided in this embodiment helps to identify the trend of declining electrical performance earlier, thereby providing a strong basis for subsequent fault prediction and risk assessment.

[0042] Step 3: Fault Risk Prediction: Based on the impact of microstructural changes on the electrical performance of the specified flexible electrical connector, a fault prediction model for the specified flexible electrical connector during use is constructed to assess whether there are potential fault risks for the specified flexible electrical connector.

[0043] In a specific embodiment, the process of constructing a fault prediction model for a specified flexible electrical connector during use is as follows: Based on the strain of the flexible material corresponding to the specified flexible electrical connector at time t, the nanostructure response intensity, and the change in electrical performance of the flexible material corresponding to the specified flexible electrical connector at time t, the expression corresponding to the fault prediction model is obtained as follows:

[0044] Fa(t) = (η1*[α(t)]2 + η2*[β(t)]3 + η3*Δχ(t))*En(t), where Fa(t) represents the probability of failure of a specified flexible electrical joint at time t, η1, η2 and η3 are the weighting coefficients corresponding to the strain of the specified flexible material, the weighting coefficient corresponding to the reaction intensity of the nanostructure and the weighting coefficient corresponding to the change in electrical performance, respectively, and En(t) represents the environmental impact factor of the external environment corresponding to the operation of the specified flexible electrical joint at time t.

[0045] It should be noted that the values ​​of η1, η2, and η3 are all greater than 0 and less than 1, and the process of setting η1, η2, and η3 is the same as... and The setting process is the same as that of the other two, and will not be described in detail here. The process of obtaining En(t) is to collect environmental information of the operating environment of the specified flexible electrical joint at time t through sensor devices, such as temperature and humidity, and then obtain En(t) through weighted calculation. The specific weighted calculation process is existing technology and will not be described in detail here.

[0046] In a specific embodiment, the process of assessing whether a designated flexible electrical connector has a potential failure risk is as follows: the failure probability of the designated flexible electrical connector at time t obtained by the failure prediction model is compared with the preset operational failure risk threshold of the flexible material corresponding to the designated flexible electrical connector. If the failure probability of the designated flexible electrical connector at time t is greater than or equal to the preset operational failure risk threshold of the flexible material corresponding to the designated flexible electrical connector, it indicates that the designated flexible electrical connector has a potential failure risk at time t; otherwise, it indicates that the designated flexible electrical connector does not have a potential failure risk at time t.

[0047] In this embodiment, by constructing a fault prediction model based on microstructural changes and electrical performance during the fault risk prediction process, it is beneficial to assess the potential fault risks of the joint during use. This allows the prediction model to output the probability of fault occurrence in a timely manner, providing data support for joint maintenance decisions, facilitating accurate assessment of the probability of fault occurrence, and helping to provide early risk warnings, reducing losses caused by sudden faults, and improving the reliability and operational safety of electrical joints.

[0048] Step 4: Real-time decision optimization: When a specified flexible electrical joint has potential failure risks, the maintenance priority of the corresponding potential failure risks of the specified flexible electrical joint is calculated based on the output results of the failure prediction model, and then the maintenance decision of the corresponding potential failure risks of the specified flexible electrical joint is generated.

[0049] In a specific embodiment, the process of calculating the maintenance priority of the potential fault risk corresponding to the specified flexible electrical joint is as follows: Based on the fault occurrence probability Fa(t) of the specified flexible electrical joint at time t obtained through the fault prediction model, the maintenance priority quantification value φ(t) corresponding to the potential fault risk of the specified flexible electrical joint is obtained by the calculation formula: φ(t)=Fa(t)*A*(1+Btime*T), where A represents the equipment importance coefficient corresponding to the specified flexible electrical joint, and Btime represents the risk weighting value caused by the extension of the operating time of the specified flexible electrical joint.

[0050] It should be noted that the equipment importance coefficient reflects the criticality of the equipment in the overall system. Suppose that in a factory, Equipment 1 and Equipment 2 are both flexible electrical connectors. Equipment 1 is the main power supply connector, and any failure will cause the entire factory to stop production. Equipment 2 is an auxiliary electrical connector, which is only used when Equipment 1 fails. Based on the above analysis, the equipment importance coefficient A of Equipment 1 is high, for example, set to 2.0, while the equipment importance coefficient of Equipment 2 is low, for example, set to 1.0.

[0051] It should also be noted that the risk weighting value resulting from extended equipment operating time reflects the increased probability of equipment failure after prolonged use. As the operating time of equipment increases, wear and tear and aging of the equipment will lead to an increased probability of failure. For example, equipment three has an operating time of 4500 hours, but 1000 hours have not been regularly inspected and maintained. Equipment four has an operating time of 2000 hours, a good maintenance record, and the most recent inspection was 100 hours ago. According to historical data analysis, equipment three has exceeded the usual maintenance cycle and its aging has accelerated. Therefore, its risk weighting value Btime is high, for example, set to 0.02, meaning that for every 100 hours of extended operation, the probability of failure increases by 2%. On the other hand, equipment four, due to good maintenance, has a low risk weighting coefficient Btime, for example, set to 0.01.

[0052] In a specific embodiment, the process of generating a maintenance decision for the potential fault risk corresponding to a specified flexible electrical connector is as follows: The maintenance priority quantification value φ(t) corresponding to the potential fault risk of the specified flexible electrical connector is compared with each threshold interval corresponding to a preset maintenance priority quantification value. Each threshold interval corresponds to a maintenance priority, which includes low priority, medium priority, and high priority. If φ(t) belongs to a certain threshold interval, the maintenance priority corresponding to that threshold interval is high priority. Then, the specified flexible electrical connector is listed as high priority, and the staff is notified to carry out maintenance within the preset safe operation time period corresponding to the high priority, thereby obtaining the maintenance decision for the potential fault risk corresponding to the specified flexible electrical connector.

[0053] In this embodiment, during the real-time decision optimization process, when there is a potential fault risk in the joint, the electrical joint fault prediction system calculates the maintenance priority through the fault prediction model and generates the corresponding maintenance decision. This realizes intelligent analysis and optimization decision-making of fault risks, effectively reduces unnecessary maintenance costs, and ensures that the joint can be maintained in a timely manner when a potential fault occurs. This facilitates accurate maintenance scheduling, avoids resource waste caused by blind maintenance, and ensures that key equipment receives timely maintenance when it is most needed, thereby improving the overall system operating efficiency.

[0054] Please see Figure 2As shown, an electrical connector fault prediction system includes the following modules: a microstructure degradation analysis module, an electrical performance response evaluation module, a fault risk prediction module, and a real-time decision optimization module.

[0055] The microstructure degradation analysis module is connected to the electrical performance response evaluation module, the electrical performance response evaluation module is connected to the fault risk prediction module, and the fault risk prediction module is connected to the real-time decision optimization module.

[0056] The microstructure degradation analysis module is used to obtain the microstructure information of a specified flexible electrical connector during use, and then analyze the changes in the microstructure of the specified flexible electrical connector during use.

[0057] The electrical performance response evaluation module is used to quantify the impact of microstructural changes on the electrical performance of a specified flexible electrical connector by collecting the corresponding electrical performance parameters based on the structural changes of the connector during use.

[0058] The fault risk prediction module is used to construct a fault prediction model for a specified flexible electrical connector during use based on the impact of microstructural changes on the corresponding electrical performance, and then assess whether the specified flexible electrical connector has potential fault risks.

[0059] The real-time decision optimization module is used to calculate the maintenance priority of the potential fault risk corresponding to the specified flexible electrical joint based on the output of the fault prediction model when the specified flexible electrical joint has potential fault risk, and then generate the maintenance decision corresponding to the potential fault risk of the specified flexible electrical joint.

[0060] This embodiment provides a method and system for predicting electrical joint failures. During the microstructure degradation analysis process, by acquiring microstructure information of a specified flexible electrical joint during use, such as initial strain, strain response rate, and nanostructure response sensitivity, it is beneficial to evaluate the microstructure changes of the joint in actual use in detail. Obtaining microstructure information through microscopy and dynamic tensile experiments helps to accurately capture the microstructure changes of the joint during use, which is conducive to identifying potential material degradation problems in advance, avoiding joint failure due to material fatigue, and thus improving the service life and safety of electrical joints.

[0061] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for predicting electrical joint faults, characterized in that, Includes the following steps: S1. Microstructure degradation analysis: Obtain the microstructure information of the specified flexible electrical connector during use, and then analyze the changes in the microstructure of the specified flexible electrical connector during use. S2. Electrical performance response assessment: Based on the structural changes of the specified flexible electrical joint during use, and by collecting the corresponding electrical performance parameters of the specified flexible electrical joint, the impact of microstructural changes on the corresponding electrical performance of the specified flexible electrical joint is quantified. S3. Fault Risk Prediction: Based on the impact of microstructural changes on the electrical performance of the specified flexible electrical joint, a fault prediction model for the specified flexible electrical joint during use is constructed to assess whether the specified flexible electrical joint has potential fault risks. S4. Real-time decision optimization: When a specified flexible electrical joint has potential failure risks, the maintenance priority of the corresponding potential failure risks of the specified flexible electrical joint is calculated based on the output results of the failure prediction model, and then the maintenance decision of the corresponding potential failure risks of the specified flexible electrical joint is generated.

2. The method for predicting electrical joint faults according to claim 1, characterized in that, In step S1, obtaining the microstructure information of the specified flexible electrical connector during use includes the following steps: S11. Microstructure information includes initial strain, strain response rate, initial response intensity, nanostructure response sensitivity, and intrinsic vibration frequency. A control sample with the same model as the specified flexible electrical connector is selected. The original length and deformation of the flexible material surface of the control sample corresponding to the specified flexible electrical connector are observed using a microscope. The ratio of deformation to original length is used to obtain the initial strain. The strain response rate experimental data are measured through dynamic tensile testing. The material strain of the control sample is corresponding to each experimental time point of the strain response rate experimental data. The change curve of material strain versus experimental time points is then fitted to obtain the strain response rate. S12. The structural information of the control sample corresponding to the specified flexible electrical connector is determined by microscopy when it is not subjected to external force, thereby obtaining the initial reaction intensity. The reaction of the control sample corresponding to the specified flexible electrical connector is fitted when it is subjected to external force, thereby obtaining the nanostructure reaction sensitivity. At the nanoscale, the resonance frequency of the flexible material corresponding to the control sample is measured by applying external excitation to the control sample corresponding to the specified flexible electrical connector, thereby obtaining the intrinsic vibration frequency.

3. The method for predicting electrical joint faults according to claim 2, characterized in that, In step S1, the microstructure changes of the specified flexible electrical joint during use are analyzed. The specific process is as follows: By establishing a strain accumulation model and a nanostructure dynamic response model, the microstructure changes of a specified flexible electrical connector during use can be evaluated. The expression corresponding to the strain accumulation model is: α(t)=α0*(1-exp(-v*T)), where α(t) represents the strain of the flexible material of the specified flexible electrical connector at time t, where α0 and v represent the initial strain and strain response rate of the specified flexible electrical connector during use, T represents the usage time of the specified flexible electrical connector, and the time between the installation of the specified flexible electrical connector and time t is T. exp(-v*T) is an exponential function. The expression corresponding to the dynamic response model of nanostructures is: β(t) = β0*(1-λ*cos(f*T)), where β(t) represents the nanostructure reaction intensity of the flexible material corresponding to the specified flexible electrical connector at time t, β0, λ and f represent the initial reaction intensity, nanostructure reaction sensitivity and intrinsic vibration frequency of the specified flexible electrical connector during use, and cos(f*T) represents the cosine function, with the value range of cos(f*T) being [-1, 1].

4. The method for predicting electrical joint faults according to claim 3, characterized in that, In step S2, the electrical performance parameters corresponding to the specified flexible electrical joint are collected. The specific process is as follows: The electrical performance parameters include the initial contact resistance, the electrical performance variation coefficient affecting the contact resistance, and the influence coefficient of electrical performance variation on the contact resistance. Under no external load, the initial contact resistance of the flexible material corresponding to the specified flexible electrical joint is determined by standard electrical performance testing, and the electrical performance variation coefficient affecting the contact resistance and the influence coefficient of electrical performance variation on the contact resistance are obtained by fitting experimental data.

5. The method for predicting electrical joint faults according to claim 4, characterized in that, In step S2, the impact of microstructure changes on the electrical performance of a specified flexible electrical connector is quantified. The specific process is as follows: Based on the strain of the flexible material and the nanostructure response intensity corresponding to the specified flexible electrical joint at time t, the following formula is used for calculation: The change in electrical performance Δχ(t) of the flexible material corresponding to a specified flexible electrical joint at time t is obtained, which is the change in electrical performance Δχ(t) of the specified flexible electrical joint after a service time T. and These are the weighting factors for the dependent variable and the weighting factor for the reaction intensity of the nanostructure, respectively. By using the dynamic resistance evolution formula corresponding to the specified flexible electrical joint: δ(t)=δ0+μ*(1+κ*Δχ(t)), the resistance δ(t) of the specified flexible electrical joint at time t can be obtained, where δ0, μ and κ represent the initial contact resistance, the electrical performance change coefficient affecting the contact resistance and the influence coefficient of the electrical performance change on the contact resistance, respectively.

6. The method for predicting electrical joint faults according to claim 5, characterized in that, In step S3, a fault prediction model for the specified flexible electrical joint during use is constructed. The specific process is as follows: According to the strain amount of the flexible material corresponding to the specified flexible electrical joint at the time t, the nanostructure reaction strength, and the electrical performance change amount of the specified flexible electrical joint at the time t, the expression corresponding to the fault prediction model is: Fa(t) = (η1*[α(t)] 2 +η2*[β(t)] 3 +η3*Δχ(t))*En(t), Fa(t) represents the failure probability corresponding to the specified flexible electrical joint at the time t, η1, η2, and η3 are respectively the weight coefficient corresponding to the strain amount of the flexible material, the weight coefficient corresponding to the nanostructure reaction strength, and the weight coefficient corresponding to the electrical performance change amount, and En(t) represents the environmental influence factor of the external environment corresponding to the specified flexible electrical joint at the time t.

7. The method for predicting electrical joint faults according to claim 6, characterized in that, In step S3, the potential failure risk of the specified flexible electrical joint is assessed, and the specific process is as follows: The failure probability of a specified flexible electrical joint at time t, obtained through the failure prediction model, is compared with the preset operational failure risk threshold of the flexible material corresponding to the specified flexible electrical joint. If the failure probability of the specified flexible electrical joint at time t is greater than or equal to the preset operational failure risk threshold of the flexible material corresponding to the specified flexible electrical joint, it indicates that the specified flexible electrical joint has a potential failure risk at time t; otherwise, it indicates that the specified flexible electrical joint does not have a potential failure risk at time t.

8. The method for predicting electrical joint faults according to claim 7, characterized in that, In step S4, the maintenance priority for potential fault risks corresponding to a specified flexible electrical joint is calculated, and the specific process is as follows: Based on the fault occurrence probability Fa(t) of the specified flexible electrical joint at time t obtained through the fault prediction model, the following formula is used for calculation: φ(t)=Fa(t)*A*(1+B time *T), to obtain the maintenance priority quantification value φ(t) of the potential fault risk corresponding to the specified flexible electrical joint, where A represents the equipment importance coefficient corresponding to the specified flexible electrical joint, B time This represents the risk weighting value resulting from the extended operating time of the specified flexible electrical joint.

9. The method for predicting electrical joint faults according to claim 8, characterized in that, In step S4, maintenance decisions are generated for potential fault risks corresponding to a specified flexible electrical joint. The specific process is as follows: The maintenance priority quantification value φ(t) corresponding to the potential fault risk of the specified flexible electrical joint is compared with the preset maintenance priority quantification value corresponding to each threshold interval. Each threshold interval corresponds to a maintenance priority, which includes low priority, medium priority and high priority. If φ(t) belongs to a certain threshold interval, the maintenance priority corresponding to that threshold interval is high priority. Then the specified flexible electrical joint is listed as high priority, and the staff is notified to carry out maintenance within the preset safe operation time period corresponding to the high priority. This is how the maintenance decision for the potential fault risk of the specified flexible electrical joint is obtained.

10. An electrical joint fault prediction system based on the electrical joint fault prediction method according to any one of claims 1-9, characterized in that, Includes the following modules: The microstructure degradation analysis module is used to obtain the microstructure information of a specified flexible electrical connector during use, and then analyze the changes in the microstructure of the specified flexible electrical connector during use. The electrical performance response evaluation module is used to quantify the impact of microstructural changes on the electrical performance of a specified flexible electrical joint by collecting the corresponding electrical performance parameters of the specified flexible electrical joint based on the structural changes of the joint during use. The fault risk prediction module is used to construct a fault prediction model for a specified flexible electrical joint during use based on the impact of microstructural changes on the corresponding electrical performance, and then assess whether the specified flexible electrical joint has potential fault risks. The real-time decision optimization module is used to calculate the maintenance priority of the potential fault risk corresponding to the specified flexible electrical joint based on the output of the fault prediction model when the specified flexible electrical joint has potential fault risk, and then generate the maintenance decision corresponding to the potential fault risk of the specified flexible electrical joint.

Images