A method, device and equipment for evaluating performance of a multi-layer polymer aluminum capacitor

Abstract

The application provides a performance evaluation method, device and equipment of a multilayer polymer aluminum capacitor, relates to the technical field of electronic component performance detection and evaluation, and aims to solve the technical problem of the deficiency of the reliability analysis method in the related art under complex working conditions and new material requirements. The method comprises the following steps: collecting key degradation indexes of the multilayer polymer aluminum capacitor under high-frequency cyclic charging and discharging conditions; performing Gamma random process modeling based on the key degradation indexes to obtain a degradation model; solving unknown parameters in the degradation model by using a Bayesian parameter estimation method to obtain a parameter estimation result; establishing a reliability evaluation model of the multilayer polymer aluminum capacitor according to the parameter estimation result, and determining the reliability of the multilayer polymer aluminum capacitor based on the reliability evaluation model.

Description

Technical Field

[0001] This application relates to the field of electronic component performance testing and evaluation technology, and in particular to a method, apparatus and equipment for evaluating the performance of multilayer polymer aluminum capacitors. Background Technology

[0002] As the performance requirements of electronic devices continue to increase, the application of multilayer polymer aluminum capacitors is gradually expanding to high-frequency, high-reliability scenarios, such as electronic control systems for new energy vehicles and high-frequency switching power supplies. Through the combination of aluminum foil anode and conductive polymer cathode, they exhibit significant advantages in low ESR, high temperature resistance, and miniaturization, providing crucial support for electronic devices.

[0003] However, reliability analysis of multilayer polymer aluminum capacitors in practical applications still faces many challenges. Existing analytical methods mainly rely on single-variable models, such as the Arrhenius model, which typically predicts lifespan based solely on environmental factors like temperature, while insufficiently considering the coupled effects of multiple stresses such as voltage, thermal stress, and mechanical vibration. This leads to a discrepancy between laboratory test results and actual operating conditions. Furthermore, conventional electrical performance testing methods, such as leakage current and capacitance measurements, often only identify anomalies after failure, failing to reflect early performance degradation in real time. Destructive testing may be necessary to further expose potential risks. Data-driven models, such as Weibull distribution and Monte Carlo simulations, can assist analysis to some extent, but their high dependence on historical data limits their adaptability to new materials or processes, especially their insufficient generalization ability under small sample conditions.

[0004] These characteristics make reliability assessment of multilayer polymer aluminum capacitors more complex. Their long-term performance is influenced by various factors, including material properties, structural design, and operating environment. In some specialized applications, the requirements for long-term reliability are particularly stringent. However, current data accumulation on long-term performance of multilayer polymer aluminum capacitors is limited, and the failure mechanisms are not fully understood. Traditional analytical methods exhibit limitations in addressing these requirements. Therefore, exploring reliability assessment methods based on degradation performance data is of great significance for improving the safety and reliability of electronic devices in practical applications. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method, apparatus, and equipment for evaluating the performance of multilayer polymer aluminum capacitors, thereby resolving the shortcomings of related technologies in reliability analysis under complex operating conditions and the need for new materials.

[0006] To achieve the above technical objectives, this application provides the following technical solution:

[0007] Firstly, this specification provides a method for evaluating the performance of multilayer polymer aluminum capacitors, including:

[0008] Key degradation indicators of multilayer polymer aluminum capacitors under high-frequency cyclic charge-discharge conditions were collected.

[0009] A degradation model is obtained by modeling a Gamma stochastic process based on key degradation indicators;

[0010] The unknown parameters in the degradation model are solved using the Bayesian parameter estimation method to obtain the parameter estimation results;

[0011] A reliability assessment model for multilayer polymer aluminum capacitors is established based on the parameter estimation results, and the reliability of multilayer polymer aluminum capacitors is determined based on the reliability assessment model.

[0012] This approach systematically collects degradation data of multilayer polymer aluminum capacitors (MLFCs) under high-frequency cyclic charge-discharge conditions to obtain key degradation indices, providing fundamental data support for subsequent modeling. Based on these key degradation indices, a Gamma stochastic process is used to mathematically model the degradation behavior of MLFCs, thus describing the trend of their performance changes over time. Furthermore, a Bayesian parameter estimation method is combined to efficiently solve the unknown parameters in the degradation model, ensuring that the model accurately reflects the actual degradation situation. Finally, a reliability assessment model is used to quantify the reliability level of MLFCs, providing a scientific basis for the safety and stability of electronic devices. This method solves the problem of traditional analysis methods relying solely on single-variable models or insufficient historical data, significantly improving the accuracy of reliability assessment for MLFCs under complex operating conditions.

[0013] In one embodiment, key degradation indicators of multilayer polymer aluminum capacitors under high-frequency charge-discharge cycles are collected, including:

[0014] Data on multiple degradation parameters of multilayer polymer aluminum capacitors under high-frequency cyclic charge-discharge conditions were obtained. Among these multiple degradation parameters, at least capacitor temperature, capacitance, loss factor, capacitor humidity, and leakage current were obtained.

[0015] Calculate the weight of each degradation parameter data, and based on the weight, select a preset number of degradation parameter data as key degradation indicators.

[0016] This approach ensures the diversity and completeness of data sources by comprehensively collecting data on various degradation parameters of multilayer polymer aluminum capacitors under high-frequency charge-discharge cycles. For each degradation parameter, its weight is calculated to quantify its impact on overall performance, thereby identifying the most representative key degradation indicators. This process avoids evaluation bias caused by fluctuations in a single parameter, improves data processing efficiency, and provides a high-quality data foundation for subsequent modeling.

[0017] In one implementation, a degradation model is obtained by modeling a Gamma stochastic process based on key degradation indices, including:

[0018] Each key degradation index was fitted using a nonlinear regression method to obtain the fitting results.

[0019] The fitting results were used as input variables for the degradation model, and the degradation behavior of multilayer polymer aluminum capacitors was described using the Gamma stochastic process.

[0020] The degradation distribution of multilayer polymer aluminum capacitors at different time points was determined by using the probability density function of the Gamma distribution.

[0021] Based on the degradation distribution of multilayer polymer aluminum capacitors at different time points, the reliability expression of multilayer polymer aluminum capacitors is determined, and the degradation model is obtained.

[0022] This approach first employs a nonlinear regression method to individually fit each key degradation index, capturing its specific patterns of change over time. Then, using the fitting results as input variables, a Gamma stochastic process is introduced to model the overall degradation behavior of multilayer polymer aluminum capacitors. The probability density function of the Gamma distribution accurately describes the degradation distribution characteristics of the multilayer polymer aluminum capacitors at different time points, thereby achieving dynamic prediction of its performance degradation trend. Based on this, a reliability expression is derived by combining the degradation distribution data, generating a complete degradation model. This process effectively solves the problem that traditional methods struggle to reflect early performance degradation in real time, significantly improving the accuracy and adaptability of the assessment.

[0023] In one implementation, the unknown parameters in the degradation model are solved using the Bayesian parameter estimation method to obtain parameter estimation results, including:

[0024] Construct a likelihood function based on the unknown parameters in the reliability expression of the degradation model;

[0025] By combining expert experience with historical data, a prior distribution is set for the unknown parameters;

[0026] The posterior distribution is calculated using Bayes' theorem, and the Markov chain Monte Carlo method is used to sample the posterior distribution to obtain the optimal estimate of the unknown parameters.

[0027] This approach first clarifies the specific form of the unknown parameters based on the reliability expression in the degradation model and constructs the corresponding likelihood function. Then, combining expert experience and existing historical data, a reasonable prior distribution is set for the unknown parameters to ensure the model has some initial guiding information. Using Bayes' theorem, the prior distribution is combined with the likelihood function to calculate the posterior distribution, thus reflecting the probability distribution characteristics of the unknown parameters under current data conditions. Finally, the Markov chain Monte Carlo method is used to sample the posterior distribution and extract the optimal estimate, completing the solution process for the unknown parameters. This method demonstrates strong generalization ability under small sample conditions and is particularly suitable for performance evaluation scenarios of novel materials or processes.

[0028] In one implementation, a reliability assessment model for multilayer polymer aluminum capacitors is established based on parameter estimation results and a degradation model, including:

[0029] Based on the reliability expression in the degradation model and the optimal estimates of the unknown parameters, a reliability evaluation model for multilayer polymer aluminum capacitors is established.

[0030] This approach combines the reliability expression in the degradation model with the optimal estimates of unknown parameters to generate a complete reliability assessment model. This model can be directly used to quantify the reliability level of multilayer polymer aluminum capacitors at different time points, providing a scientific basis for subsequent performance evaluation and maintenance decisions. This process solves the problem that traditional methods struggle to reflect early performance degradation in real time, significantly improving the accuracy and adaptability of the assessment.

[0031] In one embodiment, the method further includes;

[0032] The failure probability of multilayer polymer aluminum capacitors is calculated by setting a failure threshold based on a reliability assessment model.

[0033] Failure probability curves were plotted based on the failure probability to analyze the reliability trends of multilayer polymer aluminum capacitors at different time points.

[0034] This solution first sets a failure threshold based on a reliability assessment model to determine whether a multilayer polymer aluminum capacitor has reached a failure state. Then, it calculates the failure probability of the multilayer polymer aluminum capacitor at different time points and plots the failure probability curve. By analyzing the failure probability curve, the reliability trend of the multilayer polymer aluminum capacitor can be intuitively understood, providing an important reference for subsequent maintenance planning. This process enables dynamic monitoring of the long-term performance of multilayer polymer aluminum capacitors, significantly improving their safety and reliability in practical applications.

[0035] In one embodiment, the method further includes:

[0036] Based on the failure probability curve, determine the inflection point of reliability decline in multilayer polymer aluminum capacitors;

[0037] When the reliability of a multilayer polymer aluminum capacitor falls below the reliability warning threshold, the warning mechanism is triggered.

[0038] This solution, through in-depth analysis of the failure probability curve, identifies the inflection point of reliability degradation for multilayer polymer aluminum capacitors—the critical time point when their performance begins to significantly degrade. When the reliability of a multilayer polymer aluminum capacitor is detected to be below a preset warning threshold, an early warning mechanism is immediately triggered, alerting relevant personnel to take necessary maintenance measures. This process effectively prevents sudden failures caused by performance degradation and significantly improves the operational safety of multilayer polymer aluminum capacitors under complex operating conditions.

[0039] In one embodiment, the method further includes:

[0040] Calculate the mean time between failures (MTBF) of the multilayer polymer aluminum capacitor based on the failure probability curve.

[0041] The evaluation results are determined based on the mean time between failures (MTBF) and are used to assess the lifespan of multilayer polymer aluminum capacitors.

[0042] Based on the assessment results, a maintenance plan for the multilayer polymer aluminum capacitors was determined.

[0043] This solution first calculates the mean time between failures (MTBF) of multilayer polymer aluminum capacitors based on the failure probability curve, serving as a crucial reference indicator for their service life. Subsequently, based on the MTBF, the overall service life of the multilayer polymer aluminum capacitors is assessed, and a scientifically sound maintenance plan is developed accordingly. This process not only optimizes the operating efficiency of multilayer polymer aluminum capacitors but also significantly reduces maintenance costs, providing a strong guarantee for their long-term reliability in practical applications.

[0044] Secondly, embodiments of the present invention provide a performance evaluation device for multilayer polymer aluminum capacitors, comprising:

[0045] The acquisition unit is used to acquire key degradation indicators of multilayer polymer aluminum capacitors under high-frequency cyclic charge and discharge conditions.

[0046] The first processing unit is used to model the Gamma stochastic process based on key degradation indicators to obtain the degradation model;

[0047] The defined unit is used to solve for the unknown parameters in the degradation model using the Bayesian parameter estimation method, and obtain the parameter estimation results;

[0048] The second processing unit is used to establish a reliability assessment model for multilayer polymer aluminum capacitors based on the parameter estimation results, and to determine the reliability of multilayer polymer aluminum capacitors based on the reliability assessment model.

[0049] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the performance evaluation method for multilayer polymer aluminum capacitors described in the second aspect or any corresponding embodiment thereof.

[0050] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the performance evaluation method for multilayer polymer aluminum capacitors as described in any of the preceding claims.

[0051] Fifthly, embodiments of the present invention provide a computer program product or a computer program, the computer program product including a computer program stored in a computer-readable storage medium; a processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, it implements the performance evaluation method for multilayer polymer aluminum capacitors as described in any of the preceding claims. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in this application 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 embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0053] Figure 1 A flowchart illustrating a performance evaluation method for a multilayer polymer aluminum capacitor provided for embodiments of this specification.

[0054] Figure 2 A schematic diagram of the reliability curve of a multilayer polymer aluminum capacitor provided for the implementation of this specification;

[0055] Figure 3 A schematic diagram of the structure of a performance evaluation device for a multilayer polymer aluminum capacitor provided for the embodiments of this specification;

[0056] Figure 4 This is a schematic diagram of the structure of an electronic device provided for the implementation of this specification. Detailed Implementation

[0057] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one skilled in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0058] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0059] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0060] The performance evaluation method for multilayer polymer aluminum capacitors provided in the embodiments of this specification is described below by way of example.

[0061] This specification provides a method for evaluating the performance of multilayer polymer aluminum capacitors. First, an experimental platform needs to be built to collect degradation data of the multilayer polymer aluminum capacitors under high-frequency cyclic charge-discharge conditions. This experimental platform includes a power supply module, a load module, a temperature sensor, a capacitance meter, a loss factor meter, and a leakage current meter. The power supply module and the load module are connected by wires to form a closed loop, and the multilayer polymer aluminum capacitor is connected in series in this loop as the test object. The temperature sensor is placed on the surface of the multilayer polymer aluminum capacitor to monitor its temperature changes in real time. The capacitance meter and the loss factor meter are respectively connected to the positive and negative terminals of the multilayer polymer aluminum capacitor through probes. The leakage current meter is connected in series in the loop to record changes in leakage current. All the above equipment is connected to a computer via a data acquisition card, and all measurement data is recorded and stored in real time. The following describes the implementation of this method in detail with reference to specific application scenarios, such as... Figure 1 As shown, it includes:

[0062] S101. Collect key degradation indicators of multilayer polymer aluminum capacitors under high-frequency cyclic charge and discharge conditions.

[0063] In practice, the degradation parameters of the multilayer polymer aluminum capacitor were first obtained through high-frequency cyclic charge-discharge tests. The failure mechanism of the multilayer polymer was analyzed, and key degradation parameters affecting the reliability of the multilayer polymer aluminum capacitor were selected. During the experiment, specific parameters for high-frequency cyclic charge-discharge were first set, such as a charging voltage range of 0 volts to the rated voltage, a discharge cutoff voltage of 0 volts, a charge-discharge cycle of 1 second, and a total experimental time of 1000 hours. The failure of multilayer polymer aluminum capacitors is related to parameters such as temperature and humidity, and high-frequency use can also lead to failure. Excessively high temperatures can reduce the conductivity of the polymer electrolyte, and high-frequency use can lead to long-term heat accumulation, affecting the capacitance and equivalent series resistance. It can also accelerate the aging of the polymer electrolyte inside the capacitor. Analysis revealed that performance degradation data related to capacitor temperature, capacitance, and loss factor play a crucial role in the failure of multilayer polymer aluminum capacitors. The degradation data showed that temperature, capacitance, and loss factor all affect the performance of multilayer polymer aluminum capacitors. Therefore, studying the degree of degradation is important for evaluating the reliability of multilayer polymer aluminum capacitors. Thus, after the experiment began, the temperature, capacitance, loss factor, and leakage current of the multilayer polymer aluminum capacitors were recorded at fixed time intervals. These data formed the basis for subsequent analysis. To ensure data accuracy, all measuring instruments were calibrated before the experiment, and the ambient temperature was kept constant during the experiment to avoid interference from external factors.

[0064] After obtaining the degradation data, the next step is to preprocess it to extract key information. The first step in preprocessing is to remove outliers, which can be achieved using the 3σ criterion: calculate the standard deviation and mean of each performance parameter, and discard data points that exceed the mean plus or minus three standard deviations. Next, the remaining data is smoothed using a moving average method to reduce noise and make the data trends clearer. The preprocessed data is then further analyzed to extract its trends, such as plotting a curve of capacitance over time to observe whether there is a significant downward trend. Furthermore, the rate of change of each performance parameter needs to be calculated to analyze its impact on the reliability of the multilayer polymer aluminum capacitor.

[0065] To determine the key degradation factors affecting the reliability of multilayer polymer aluminum capacitors, a method combining random forest regression model and analytic hierarchy process was adopted.

[0066] First, a random forest regression model is used to perform feature importance analysis on the preprocessed data. The construction process of the random forest regression model is as follows: temperature, capacitance, loss factor, and leakage current are used as input variables, and their rate of change is used as the output variable to train the random forest model. The mean squared error of the random forest regression model is:

[0067] ;

[0068] in, D t It is the set of samples in node t. N t It is the number of samples in node t. y i It is a sample i The true value, It is the average value of the samples in node t.

[0069] Will D t Chinese characteristics j The values ​​are randomly arranged to generate a perturbation dataset. D t,j Its disturbance error is:

[0070] ;

[0071] in, This is the average value after using perturbation data.

[0072] After the model is trained, a feature importance score is calculated for each input variable. A higher score indicates a greater influence of that variable on the output variable. For features... j Its importance is the average of the error increments across all trees. W j :

[0073] ;

[0074] These scores were then normalized to the 0-1 range to eliminate the influence of differences in units. Specifically, T represents the total number of features, and the importance scores of all features were normalized to the [0-1] range:

[0075] ;

[0076] The normalized scores are used as elements of the judgment matrix in the analytic hierarchy process (AHP). The judgment matrix is ​​constructed using a nine-level scaling method, where the relative importance of variables is determined by the relative magnitude of the normalized scores. For example, if the normalized score for capacitance is 0.4 and the normalized score for loss factor is 0.2, then the importance ratio of capacitance to loss factor is considered to be 2. The constructed judgment matrix undergoes a consistency check. If the consistency ratio is less than 0.1, the matrix is ​​considered to have acceptable consistency; otherwise, the matrix needs to be revised until it meets the requirements. The judgment matrix is ​​constructed as follows:

[0077] ;

[0078] If n=3, then the judgment matrix is:

[0079] ;

[0080] The eigenvalues ​​λ and eigenvectors are calculated using the judgment matrix. v Perform a consistency check:

[0081] ;

[0082] Consistency ratio calculation:

[0083] ;

[0084] Here, RI is the consistency index, which can be obtained by looking up a table. If the consistency ratio CR < 0.1, the matrix passes the consistency test; otherwise, the judgment matrix needs to be readjusted. Finally, the weight values ​​of each degradation quantity are obtained by calculating the largest eigenvalue of the judgment matrix and its corresponding eigenvector. Then, based on these weight values, the few with higher weight values ​​are selected from the degradation data as key degradation indicators.

[0085] In one example, four groups of capacitors are used for calculation. The charging voltage of the first and third groups is 1.5 times the rated voltage, while the charging and discharging voltages of the second and fourth groups are the rated voltages of the capacitors. The resulting weights are shown in the table below:

[0086]

[0087] Based on the aforementioned weights, three data points with higher weights are selected from the four degradation data points as key degradation indicators. Of course, other numbers can also be selected, and this embodiment does not limit this selection.

[0088] S102. Based on key degradation indicators, Gamma stochastic process modeling is performed to obtain the degradation model.

[0089] In practice, the next step is to model the multilayer polymer aluminum capacitor using a Gamma stochastic process based on the selected key degradation parameters. The Gamma stochastic process is a non-negative, monotonically increasing stochastic process suitable for describing the degradation behavior of multilayer polymer aluminum capacitors.

[0090] Assume the number of degraded data samples obtained is n, and the sample number is... i For each sample, the degradation amount was measured m times at the same time interval, with the sequence number as follows: j ,remember X ij It is the first i The sample at the th j The degradation amount at the time of measurement, which is denoted as . t ij , X ti0 For the first i The initial degradation amount for each sample is set as follows: x 0.

[0091] We modeled the data using a Gamma stochastic process and performed preliminary processing on the degraded data. The first... i The degradation increment of each sample is expressed as:

[0092]

[0093] The obtained degradation increment data can then be represented as:

[0094] ;

[0095] The degradation time interval is: Assume the degradation process follows a Gamma process, i.e., the degradation increment follows a shape parameter of... η The scale parameter is β The Gamma distribution can be expressed as:

[0096] ;

[0097] make X (t) represents the measured value of the capacitor's performance degradation at time t, which is then expressed by the probability density function of the Gamma distribution:

[0098] ;

[0099] Then we can obtain the following probability density function for the degradation:

[0100] ;

[0101] in For the Gamma function, .

[0102] Let T represent the time when the degradation first reaches the failure threshold C, and the initial value of the degradation is... X 0. Since both the failure threshold and the initial value of degradation are constants, the reliability P of the multilayer polymer aluminum capacitor at time t is:

[0103] ;

[0104] In the above formula: ;

[0105] In summary, the reliability expression can be obtained as follows:

[0106]

[0107] Failure probability curve: ;

[0108] S103. Solve for the unknown parameters in the degradation model using the Bayesian parameter estimation method to obtain the parameter estimation results.

[0109] In practical implementation, high-reliability, long-life products are typically small-sample problems. To accurately estimate the unknown parameters in the degradation model of multilayer polymer aluminum capacitors, prior information such as expert experience and historical data is fully utilized, and a Bayesian parameter estimation method is employed for parameter estimation. To solve for the unknown parameters in the Gamma degradation model, a Bayesian parameter estimation method is used, based on the degradation increment data from the above steps.

[0110] The degradation increment, defined by the Gamma stochastic process, follows a shape parameter of... η The scale parameter is β The probability density function of the degradation increment for the Gamma distribution can be expressed as:

[0111] ;

[0112] The likelihood function for the unknown parameter is:

[0113] ;

[0114] The prior distribution is ;

[0115] Where, π ( η,β ) is the joint prior distribution of the two parameters, π( η ) and π β ) are respectively η and β Given the prior distribution, we can obtain the posterior distribution of the unknown parameters:

[0116] ;

[0117] By solving, we can obtain... η and β The parameter estimation results.

[0118] In Bayesian models, weights are typically adjusted by modifying the contribution of the likelihood function. This is because, in the Gamma distribution, the shape parameter... η and scale parameters β This determines the characteristics of the distribution. To observe the impact of different variables on the overall model, adjustments are made using a weighted likelihood function method to achieve the aforementioned objective.

[0119] The probability density function of the degradation increment is:

[0120]

[0121] The likelihood function for the unknown parameter is:

[0122] ;

[0123] The prior distribution remains unchanged, while the posterior distribution is:

[0124] ;

[0125] Where k is the degradation parameter index (k=1 for temperature, k=2 for capacitance, k=3 for loss factor). W k The weight of the k-th parameter, This represents the degradation increment for the i-th sample, the j-th measurement, and the k-th parameter.

[0126] In practical calculations, due to the complexity of the integral form of the posterior distribution, the Markov chain Monte Carlo method is typically used for approximate solutions. Through multiple iterative sampling, the estimated values ​​of η and β are finally obtained.

[0127] Continuing with the above example, the shape parameter η and scale parameter β of the four sets of capacitors are shown in the table below:

[0128]

[0129] S104. Establish a reliability assessment model for multilayer polymer aluminum capacitors based on the parameter estimation results, and determine the reliability of multilayer polymer aluminum capacitors based on the reliability assessment model.

[0130] In practice, after completing the parameter estimation, a reliability assessment model for the multilayer polymer aluminum capacitor is established based on the parameter estimation results and the reliability expression. Specifically, for the multilayer polymer aluminum capacitor in the first group in the example above, its reliability distribution model is as follows:

[0131] ;

[0132] The reliability curves of the multilayer polymer aluminum capacitor can then be plotted based on this reliability distribution model. By plotting these time points and their corresponding reliability values ​​as a curve, the trend of reliability changes over time can be visually displayed. Analysis of the reliability curves can identify key time points in the performance degradation of the multilayer polymer aluminum capacitor. For example, if the reliability curve shows a significant decrease at a certain time point, it indicates that this time point may be a turning point in performance degradation.

[0133] Using the example above, Figure 2 The figure shows the reliability curves of four groups of multilayer polymer aluminum capacitors under high-frequency charge-discharge conditions in the above example. The reliability of the first group starts to decrease from 1500 min, and drops to about 50% at around 2500 min.

[0134] After determining the critical time points for performance degradation of multilayer polymer aluminum capacitors, a failure warning threshold is further determined based on the reliability curve. The selection of the failure warning threshold must comprehensively consider the actual application scenario and safety requirements of the multilayer polymer aluminum capacitor. For example, in applications with high reliability requirements, a lower failure warning threshold can be selected to detect potential problems early. Maintenance and replacement strategies are then developed based on the failure warning threshold; for example, when reliability drops below the warning threshold, the capacitor should be immediately discontinued and replaced with a new multilayer polymer aluminum capacitor. To verify the effectiveness of the maintenance and replacement strategies, experimental verification is required. The experimental verification process is as follows: Several multilayer polymer aluminum capacitors are selected and subjected to high-frequency cyclic charge-discharge experiments according to the aforementioned experimental platform and parameter settings, and their actual service life is recorded. The actual service life is compared with the theoretical prediction results; if the error is within an acceptable range, the maintenance and replacement strategy is considered effective. By continuously optimizing the maintenance and replacement strategies, the service life of multilayer polymer aluminum capacitors can be significantly extended, thereby improving the safety and stability of electronic devices.

[0135] For example, assuming the failure probability curve shows a low failure probability within the period from t=0 to t=1500 hours, this period can be considered a stable operating period. The mean time between failures (MTBF) is calculated to be 1600 hours, based on which a scientifically sound maintenance plan can be developed. For instance, for a batch of multilayer polymer aluminum capacitors, if they exhibit high reliability in practical applications, the maintenance cycle can be appropriately extended to reduce maintenance costs.

[0136] Through the above steps, the method provided by this invention can effectively prevent sudden failures caused by performance degradation and significantly improve the operational safety of multilayer polymer aluminum capacitors under complex operating conditions. Simultaneously, by systematically collecting key degradation indicators, modeling and analyzing them, and conducting reliability assessments, this method achieves comprehensive monitoring and scientific management of capacitor performance, providing crucial protection for the safety and stability of electronic equipment.

[0137] In one exemplary embodiment of this specification, a performance evaluation device 700 for multilayer polymer aluminum capacitors is also provided, such as... Figure 3 As shown, it includes:

[0138] The acquisition unit 701 is used to acquire key degradation indicators of multilayer polymer aluminum capacitors under high-frequency cyclic charge and discharge conditions.

[0139] The first processing unit 702 is used to model the Gamma stochastic process based on key degradation indicators to obtain a degradation model;

[0140] Unit 703 is used to solve for the unknown parameters in the degradation model using the Bayesian parameter estimation method, and obtain the parameter estimation results;

[0141] The second processing unit 704 is used to establish a reliability assessment model for multilayer polymer aluminum capacitors based on the parameter estimation results, and to determine the reliability of multilayer polymer aluminum capacitors based on the reliability assessment model.

[0142] In one embodiment, the acquisition unit 701 is specifically used for:

[0143] Data on multiple degradation parameters of multilayer polymer aluminum capacitors under high-frequency cyclic charge-discharge conditions were obtained. Among these multiple degradation parameters, at least capacitor temperature, capacitance, loss factor, capacitor humidity, and leakage current were obtained.

[0144] Calculate the weight of each degradation parameter data, and based on the weight, select a preset number of degradation parameter data as key degradation indicators.

[0145] In one embodiment, the first processing unit 702 is specifically used for:

[0146] Each key degradation index was fitted using a nonlinear regression method to obtain the fitting results.

[0147] The fitting results were used as input variables for the degradation model, and the degradation behavior of multilayer polymer aluminum capacitors was described using the Gamma stochastic process.

[0148] The degradation distribution of multilayer polymer aluminum capacitors at different time points was determined by using the probability density function of the Gamma distribution.

[0149] Based on the degradation distribution of multilayer polymer aluminum capacitors at different time points, the reliability expression of multilayer polymer aluminum capacitors is determined, and the degradation model is obtained.

[0150] In one implementation, the determining unit 703 is specifically used for:

[0151] Construct a likelihood function based on the unknown parameters in the reliability expression of the degradation model;

[0152] By combining expert experience with historical data, a prior distribution is set for the unknown parameters;

[0153] The posterior distribution is calculated using Bayes' theorem, and the Markov chain Monte Carlo method is used to sample the posterior distribution to obtain the optimal estimate of the unknown parameters.

[0154] In one embodiment, the second processing unit 704 is specifically used for:

[0155] Based on the reliability expression in the degradation model and the optimal estimates of the unknown parameters, a reliability evaluation model for multilayer polymer aluminum capacitors is established.

[0156] In one embodiment, the second processing unit 704 is further configured to:

[0157] The failure probability of multilayer polymer aluminum capacitors is calculated by setting a failure threshold based on a reliability assessment model.

[0158] Failure probability curves were plotted based on the failure probability to analyze the reliability trends of multilayer polymer aluminum capacitors at different time points.

[0159] In one embodiment, the second processing unit 704 is further configured to:

[0160] Based on the failure probability curve, determine the inflection point of reliability decline in multilayer polymer aluminum capacitors;

[0161] When the reliability of a multilayer polymer aluminum capacitor falls below the reliability warning threshold, the warning mechanism is triggered.

[0162] In one embodiment, the second processing unit 704 is further configured to:

[0163] Calculate the mean time between failures (MTBF) of the multilayer polymer aluminum capacitor based on the failure probability curve.

[0164] The evaluation results are determined based on the mean time between failures (MTBF) assessment, and these results are used to determine the lifespan of multilayer polymer aluminum capacitors.

[0165] Based on the assessment results, a maintenance plan for the multilayer polymer aluminum capacitors was determined.

[0166] The performance evaluation device for multilayer polymer aluminum capacitors provided in this embodiment belongs to the same application concept as the performance evaluation method for multilayer polymer aluminum capacitors provided in the above embodiments of this application. It can execute the performance evaluation method for multilayer polymer aluminum capacitors provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects for executing the performance evaluation method for multilayer polymer aluminum capacitors. Technical details not described in detail in this embodiment can be found in the specific processing content of the performance evaluation method for multilayer polymer aluminum capacitors provided in the above embodiments of this application, and will not be repeated here.

[0167] In one exemplary embodiment of this specification, an electronic device is also provided, such as Figure 4 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call logic instructions in the memory 330 to execute a performance evaluation method for multilayer polymer aluminum capacitors, the method including:

[0168] Key degradation indicators of multilayer polymer aluminum capacitors under high-frequency cyclic charge-discharge conditions were collected.

[0169] A degradation model is obtained by modeling a Gamma stochastic process based on key degradation indicators;

[0170] The unknown parameters in the degradation model are solved using the Bayesian parameter estimation method to obtain the parameter estimation results;

[0171] A reliability assessment model for multilayer polymer aluminum capacitors is established based on the parameter estimation results, and the reliability of multilayer polymer aluminum capacitors is determined based on the reliability assessment model.

[0172] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0173] In addition to the methods, apparatus, and devices described above, the performance evaluation method for multilayer polymer aluminum capacitors provided in the embodiments of this specification can also be a computer program product, which includes computer program instructions that, when executed by a processor, cause the processor to perform the steps in the performance evaluation method for multilayer polymer aluminum capacitors according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0174] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this specification. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages.

[0175] Furthermore, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the performance evaluation method for multilayer polymer aluminum capacitors according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0176] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this specification can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0177] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The embodiments described above are merely illustrative of several implementation methods outlined in this specification. While the descriptions are specific and detailed, they should not be construed as limiting the scope of the solutions provided in this specification. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this specification, and these all fall within the scope of protection of this specification. Therefore, the scope of protection for this patent should be determined by the appended claims.

Claims

1. A method for evaluating the performance of a multilayer polymer aluminum capacitor, characterized in that, include: Key degradation indicators of multilayer polymer aluminum capacitors under high-frequency charge-discharge cycles were collected, including: Acquire multiple degradation parameter data of the multilayer polymer aluminum capacitor under high-frequency cyclic charge and discharge conditions, wherein the multiple degradation parameter data includes at least capacitor temperature, capacitance, loss factor, capacitor humidity and leakage current; calculate the weight of each degradation parameter data, and based on the weight, select a preset number of degradation parameter data as the key degradation index; Based on the aforementioned key degradation indices, a Gamma stochastic process model is performed to obtain a degradation model, including: Each of the key degradation indicators was fitted using a nonlinear regression method to obtain the fitting results. The fitting results are used as input variables for the degradation model, and the degradation behavior of the multilayer polymer aluminum capacitor is described using a Gamma stochastic process. The degradation distribution of the multilayer polymer aluminum capacitor at different time points is determined by the probability density function of the Gamma distribution. Based on the degradation distribution of the multilayer polymer aluminum capacitor at different time points, the reliability expression of the multilayer polymer aluminum capacitor is determined, thus obtaining the degradation model. The unknown parameters in the degradation model are solved using the Bayesian parameter estimation method to obtain the parameter estimation results. A reliability assessment model for multilayer polymer aluminum capacitors is established based on the parameter estimation results, and the reliability of the multilayer polymer aluminum capacitors is determined based on the reliability assessment model.

2. The method according to claim 1, characterized in that, The method of using Bayesian parameter estimation to solve for the unknown parameters in the degradation model yields parameter estimation results, including: Construct a likelihood function based on the unknown parameters of the reliability expression in the degradation model; By combining expert experience with historical data, a prior distribution is set for the unknown parameters; The posterior distribution is calculated using Bayes' theorem, and the optimal estimate of the unknown parameter is obtained by sampling the posterior distribution using the Markov chain Monte Carlo method.

3. The method according to claim 2, characterized in that, The step of establishing a reliability assessment model for the multilayer polymer aluminum capacitor based on the parameter estimation results and the degradation model includes: Based on the reliability expression in the degradation model and the optimal estimate of the unknown parameters, a reliability evaluation model for the multilayer polymer aluminum capacitor is established.

4. The method according to claim 3, characterized in that, The method further includes; Based on the reliability assessment model, a failure threshold is set, and the failure probability of the multilayer polymer aluminum capacitor is calculated. Failure probability curves are plotted based on the failure probabilities to analyze the reliability trends of multilayer polymer aluminum capacitors at different time points.

5. The method according to claim 4, characterized in that, Also includes: Based on the failure probability curve, determine the inflection point of reliability decline of the multilayer polymer aluminum capacitor; When the reliability of a multilayer polymer aluminum capacitor falls below the reliability warning threshold, the warning mechanism is triggered.

6. The method according to claim 4, characterized in that, Also includes: Calculate the mean time between failures (MTBF) of the multilayer polymer aluminum capacitor based on the failure probability curve. The evaluation result is determined based on the mean time between failures (MTBF), and the evaluation result is used to evaluate the service life of the multilayer polymer aluminum capacitor. Based on the assessment results, a maintenance plan for the multilayer polymer aluminum capacitor is determined.

7. A performance evaluation device for multilayer polymer aluminum capacitors, characterized in that, The method for evaluating the performance of a multilayer polymer aluminum capacitor according to any one of claims 1 to 6 includes: The acquisition unit is used to acquire key degradation indicators of multilayer polymer aluminum capacitors under high-frequency cyclic charge and discharge conditions. The first processing unit is used to perform Gamma stochastic process modeling based on the key degradation index to obtain a degradation model; The determination unit is used to solve for the unknown parameters in the degradation model using the Bayesian parameter estimation method to obtain the parameter estimation results; The second processing unit is used to establish a reliability evaluation model for the multilayer polymer aluminum capacitor based on the parameter estimation results, and to determine the reliability of the multilayer polymer aluminum capacitor based on the reliability evaluation model.

8. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the performance evaluation method for the multilayer polymer aluminum capacitor as described in any one of claims 1 to 6.

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