SiC MOSFET life prediction method and device based on SSA-LSTM, and storage medium
By introducing the SSA-LSTM model in SiC MOSFET lifetime prediction, the parameters are optimized to build a prediction model, and the problems of low prediction accuracy and local optimal solutions in traditional methods are solved, achieving more efficient and accurate lifetime prediction.
Patent Information
- Application Number
- CN202510069946.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-16
- Publication Date
- 2025-04-25
AI Technical Summary
The traditional SiC MOSFET lifetime prediction method is difficult to accurately predict its remaining service life, and it is easy to fall into the local optimal solution, resulting in low prediction accuracy.
The SiC MOSFET lifetime prediction method based on SSA-LSTM is adopted. By selecting the drain-source voltage as the aging characteristic parameter, the sparrow search algorithm is used to optimize the parameters of the long and short-term memory network, and an SSA-LSTM prediction model is constructed to predict the remaining life of SiC MOSFET.
It improves the efficiency of model training and prediction, enhances prediction accuracy and generalization capabilities, and can more accurately adapt to the life prediction needs under different operating conditions.
Smart Images
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Abstract
Description
Technical Field
[0001] The present application relates to the technical field of reliability prediction of power electronic equipment, and in particular to a method, device and storage medium for predicting the life of a SiC MOSFET based on SSA-LSTM. Background Art
[0002] With the rapid development of power electronics technology, SiC MOSFET, as a core component in power electronic equipment, has an important impact on the overall performance of the system. However, since the aging process of SiC MOSFET is affected by many factors, such as electrical, thermal, and mechanical stress, traditional life prediction methods often find it difficult to accurately predict its remaining service life. Therefore, developing a SiC MOSFET life prediction method based on SSA-LSTM is of great significance for improving the reliability of power electronic equipment and reducing maintenance costs. At present, some data-driven life prediction methods have been proposed, such as prediction methods based on machine learning algorithms such as neural networks and support vector machines. However, these methods still need to be improved in terms of prediction accuracy and generalization ability. At the same time, since the aging process of SiC MOSFET is complex and changeable, traditional prediction methods often find it difficult to adapt to the life prediction needs under different operating conditions.
[0003] It can be seen that when traditional data-driven models are used to predict the life of SiC MOSFET, there is often a strong dependence on data quality and it is easy to fall into the local optimal solution, resulting in low prediction accuracy. Summary of the invention
[0004] Based on this, it is necessary to provide a SiC MOSFET life prediction method, device and storage medium based on SSA-LSTM to address the above technical problems.
[0005] This manual adopts the following technical solutions:
[0006] This specification provides a SiC MOSFET lifetime prediction method based on SSA-LSTM, including:
[0007] The drain-source voltage is selected as the aging characteristic parameter of SiC MOSFET, and the voltage data is obtained in real time by using a sensor during the aging test;
[0008] Preprocess the collected voltage data to eliminate outliers, supplement missing values, and smooth the data;
[0009] The parameters of the long short-term memory network LSTM are optimized through the sparrow search algorithm SSA, and the SSA-LSTM prediction model is constructed;
[0010] Initialize the SSA-LSTM model and input voltage data for training, establish the mapping relationship between SiC MOSFET life and voltage, and realize the prediction of the remaining life of SiC MOSFET.
[0011] Preferably, the sensor is used to obtain voltage data in real time during the aging test, specifically including: based on the accelerated aging test of SiC MOSFET, according to the working principle and failure mechanism of SiC MOSFET, key voltage parameters closely related to its life are selected and collected through voltage sensors.
[0012] Preferably, the preprocessing of the collected voltage data specifically includes: cleaning the collected raw voltage data, using statistical methods to identify and eliminate outliers in the data, using filters to remove high-frequency noise, and filling by interpolation, or deleting records containing missing values according to specific circumstances.
[0013] Preferably, the optimization of the parameters of the long short-term memory network LSTM by using the sparrow search algorithm SSA specifically includes:
[0014] The optimal parameter configuration searched by the SSA algorithm is applied to the LSTM model to build an SSA-LSTM prediction model. These parameters include learning rate, number of hidden units, number of iterations, etc.
[0015] Preferably, the initializing the SSA-LSTM model specifically includes:
[0016] Initializing the LSTM parameters, including the number of neurons, weights, biases, the number of layers of the LSTM model, and the selection of an internal optimizer;
[0017] The initialized SSA algorithm parameters include the size of the sparrow population, the ratio of predators (discoverers) to joiners, and the number of iterations of the algorithm.
[0018] Preferably, the input voltage parameter is trained, specifically including:
[0019] The preprocessed voltage data is divided into a training set and a validation set. The constructed SSA-LSTM prediction model is trained using the training set data. According to the performance indicators on the validation set, the parameters of the SSA algorithm are continuously adjusted to improve the optimization process.
[0020] Preferably, the prediction of the remaining life of the SiC MOSFET is achieved by:
[0021] In each iteration, the SSA algorithm is used to optimize the parameters of the LSTM network. In each iteration, the SSA algorithm calculates the fitness value based on the position of each sparrow in the current population (i.e., the parameter combination of the LSTM model), and updates the position of the sparrow based on the fitness value to find a better LSTM model parameter combination. Repeat the above steps until the preset number of iterations is reached or the convergence condition is met, and the result of the remaining life of the SiC MOSFET is obtained.
[0022] The present specification provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned SiC MOSFET life prediction method based on SSA-LSTM is implemented.
[0023] The present specification provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned SiCMOSFET life prediction method based on SSA-LSTM is implemented.
[0024] A SiC MOSFET life prediction method based on SSA-LSTM provided in this specification has the following beneficial effects compared with the prior art:
[0025] The present invention combines the advantages of the SSA algorithm and the LSTM network, optimizes the LSTM network parameters through the SSA algorithm, and constructs an SSA-LSTM prediction model, which can effectively search for the optimal solution or a parameter combination close to the optimal solution in a complex parameter space, and find a better solution in a shorter time, thereby improving the efficiency of model training and prediction. At the same time, it can be widely used in the semiconductor industry, new energy vehicles, industrial automation and other fields, providing strong support for the health management and maintenance of equipment. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:
[0027] Figure 1 A schematic diagram of a SiC MOSFET life prediction method provided in this specification;
[0028] Figure 2 It is a simple circuit platform design for the DC power cycle test in this embodiment;
[0029] Figure 3 The corresponding SiC MOSFET drain-source voltage is collected when the constant current is 15A and the temperature is 150°C in this embodiment;
[0030] Figure 4 is the drain-source voltage of the SiC MOSFET after data processing in this embodiment;
[0031] Figure 5 This is the LSTM network topology in this embodiment;
[0032] Figure 6 This is a graph showing the life prediction results of SiC MOSFET based on the traditional LSTM model in this embodiment;
[0033] Figure 7 This is a graph showing the life prediction results of SiC MOSFET based on the SSA-LSTM model in this embodiment. DETAILED DESCRIPTION
[0034] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this application will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in the specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0035] SiC MOSFET lifetime prediction based on traditional data-driven models usually trains and verifies models on specific data sets, which results in limited generalization capabilities of the models to other data sets or operating conditions. When the operating environment or conditions of the SiC MOSFET change, these models may not provide accurate prediction results.
[0036] Therefore, this specification provides a SiC MOSFET life prediction method based on SSA-LSTM, which can effectively search for the optimal solution or a parameter combination close to the optimal solution in a complex parameter space, and find a better solution in a shorter time, thereby improving the efficiency of model training and prediction, and solving the problems of strong dependence on data quality when predicting SiC MOSFET life under traditional data-driven models, and the traditional data-driven models are prone to fall into local optimal solutions, resulting in low prediction accuracy.
[0037] In a SiC MOSFET life prediction method based on SSA-LSTM proposed in this specification, an SSA-LSTM prediction model is constructed to predict the life of SiC MOSFET. The model is an optimization and prediction method that combines the Sparrow Search Algorithm (SSA) and the Long Short-Term Memory Network (LSTM). The Sparrow Search Algorithm is a bionic optimization algorithm that simulates the foraging behavior of sparrows in nature. It divides the search space into different areas by simulating the foraging, vigilance and transfer behavior of the sparrow group, and uses the mechanism of discoverers and joiners to balance global exploration and local development. The discoverer is responsible for guiding the search direction, while the joiner follows and may surpass the discoverer. At the same time, the entire group will collectively adjust its strategy when facing the threat of predators. This flexible group collaboration mechanism enables SSA to effectively solve complex optimization problems. LSTM linearly transmits information along the time axis by introducing gating mechanisms such as forget gates, input gates and output gates, similar to a continuously flowing information conveyor belt, ensuring that long and short-term memory is maintained. Among them, the forget gate is responsible for controlling the information to be forgotten from the cell state, the input gate determines whether new information should be added to the cell state, and the output gate controls whether the information should be output as a hidden state to the subsequent layer or used for the final prediction.
[0038] Typically, traditional LSTM networks are often used as a method for predicting the life of IGBTs. However, this method may have a slow training and inference process in large-scale data sets or complex models, requiring more computing resources. High complexity may also cause the model to be more prone to overfitting problems during training, and it has a strong dependence on data quality. In addition, the training process may fall into a local optimal solution, resulting in an inability to provide accurate prediction results.
[0039] Compared with traditional data-driven prediction models such as LSTM, the SSA-LSTM model provided in this manual combines the advantages of the sparrow search algorithm (SSA) and the long short-term memory network (LSTM), and optimizes the key parameters of the LSTM network through SSA to improve the prediction accuracy and generalization ability of the LSTM model. SSA simulates the foraging behavior of sparrow groups, finds the optimal parameter combination through iterative optimization, and then these optimized parameters are used to build the LSTM network, so that LSTM can process time series data more effectively and capture long-term dependencies in the data, thereby achieving more accurate predictions.
[0040] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.
[0041] Figure 1The following is a flow chart of a SiC MOSFET life prediction method based on SSA-LSTM in this specification, which specifically includes the following steps:
[0042] S101: Select drain-source voltage as the aging characteristic parameter of SiC MOSFET, and use a sensor to obtain voltage data in real time during the aging test, including:
[0043] Based on the accelerated aging test of SiC MOSFET, according to the working principle and failure mechanism of SiC MOSFET, key voltage parameters closely related to its life are selected and collected through voltage sensors.
[0044] S102: Preprocessing the collected voltage data to eliminate abnormal values, supplement missing values, and smooth the data, specifically including:
[0045] The collected raw voltage data is cleaned, and outliers in the data are identified and eliminated using statistical methods. High-frequency noise is removed using filters, and interpolation is used to fill in the data, or records with missing values are deleted according to specific circumstances.
[0046] S103: Optimize the parameters of the long short-term memory network LSTM through the sparrow search algorithm SSA and build an SSA-LSTM prediction model, which includes:
[0047] The optimal parameter configuration searched by the SSA algorithm is applied to the LSTM model to build an SSA-LSTM prediction model. These parameters include learning rate, number of hidden units, number of iterations, etc.
[0048] S104: Initialize the SSA-LSTM model and input voltage data for training, establish a mapping relationship between SiC MOSFET life and voltage, and predict the remaining life of SiC MOSFET, including:
[0049] Initialize the LSTM parameters, including the number of neurons, weights, biases, the number of layers of the LSTM model, and the selection of the internal optimizer.
[0050] Initialize the SSA algorithm parameters, including the size of the sparrow population, the ratio of predators (discoverers) to joiners, and the number of iterations of the algorithm.
[0051] The input voltage parameters are trained, including dividing the preprocessed voltage data into a training set and a validation set, using the training set data to train the constructed SSA-LSTM prediction model, and continuously adjusting the parameters of the SSA algorithm according to the performance indicators on the validation set to improve the optimization process.
[0052] In each iteration, the SSA algorithm is used to optimize the parameters of the LSTM network. In each iteration, the SSA algorithm calculates the fitness value based on the position of each sparrow in the current population (i.e., the parameter combination of the LSTM model), and updates the position of the sparrow based on the fitness value to find a better LSTM model parameter combination. Repeat the above steps until the preset number of iterations is reached or the convergence condition is met, and the result of the remaining life of the SiC MOSFET is obtained.
[0053] It should be noted that the SiC MOSFET accelerated aging test is a test method that simulates the power cycle conditions of the SiC MOSFET in actual work to accelerate its aging process. The experiment continuously applies power to the SiC MOSFET to make it switch periodically between the on and off states, thereby using the thermal stress generated inside the device to impact the SiC MOSFET and accelerate its aging failure. The power cycle aging experiment can effectively evaluate the reliability and life of the SiC MOSFET under long-term use, and provide a basis for product design and optimization. The simple circuit platform design of the power cycle test is as follows: Figure 2 shown.
[0054] Example
[0055] The SiC MOSFET life prediction method proposed in this embodiment adds the SSA optimization algorithm to the traditional SiC MOSFET life prediction method. Under this premise, the above scheme uses the position of each sparrow to calculate the fitness, and updates the position of the sparrow according to the fitness value to find the optimal solution to deal with the problem that the data quality is highly dependent on the life prediction of SiC MOSFET under the traditional data-driven model, and it is easy to fall into the local optimal solution; during the prediction process, the SSA-LSTM algorithm will continuously update the parameters of the LSTM network to adapt to the changes in the input data and improve the accuracy of the prediction.
[0056] The flow chart of SiC MOSFET lifetime prediction method based on SSA-LSTM model is as follows: Figure 1 As shown, this embodiment specifically includes the following steps:
[0057] Step 1: Select the drain-source voltage as the aging characteristic parameter of SiC MOSFET, and use the sensor to obtain voltage data in real time during the aging test.
[0058] The aging test process of SiC MOSFET in this embodiment specifically includes:
[0059] The device supplies power to the aging board or test board, and applies a periodically changing load current to the SiC MOSFET to simulate the junction temperature changes in actual use. In the test, the device is repeatedly heated and cooled by controlling the opening and closing of the current to accelerate its aging process, and the changes in the drain-source voltage are monitored in real time to evaluate the packaging reliability and aging characteristics of the device.
[0060] In addition, the key parameters of SiC MOSFET collected are the corresponding SiC MOSFET drain-source voltage when the constant current is 15A and the temperature is 150℃, such as Figure 3 shown.
[0061] Step 2: Preprocess the collected voltage data to eliminate outliers, supplement missing values, and smooth the data, including:
[0062] The collected voltage outliers are eliminated. This step aims to identify and eliminate values that are significantly deviated from the normal data range. These outliers may be caused by measurement errors, equipment failures, or external interference. A reasonable threshold range can be set, and data points outside this range are regarded as outliers and eliminated. Alternatively, statistical methods such as box plots can be used to automatically identify and eliminate outliers.
[0063] To supplement the missing values that may exist in the data, the missing data will affect the accuracy and reliability of subsequent analysis. The method to deal with missing values is to directly delete the records containing missing values (if the missing ratio is not high) and use interpolation methods (such as linear interpolation, polynomial interpolation, etc.) to estimate the missing values based on adjacent data points.
[0064] Smoothing the data helps reduce random noise in the data and make the data change trend clearer. The mean filtering method is commonly used. The basic principle of mean filtering is to replace the pixel values in the original image with the mean, that is, for the current pixel point (x, y) to be processed, select a template composed of several pixels of its neighbors, calculate the mean of all pixels in the template, and then assign the mean to the current pixel point (x, y) as the grayscale g(x, y) of the processed image at this point. Its expression is:
[0065]
[0066] In the above expression, m is the total number of pixels in the template including the current pixel. After data processing, the drain-source voltage of the SiC MOSFET is as follows: Figure 4 shown.
[0067] Step 3: Optimize the parameters of the long short-term memory network LSTM through the sparrow search algorithm SSA and build an SSA-LSTM prediction model.
[0068] It should be noted that the advantage of combining SSA and LSTM is that the global search capability and high efficiency of the sparrow search algorithm SSA can make up for the shortcomings of the LSTM network in parameter optimization; and the complex structure and powerful learning ability of the LSTM network can make full use of the optimization results of the dung beetle optimization algorithm SSA to further improve the performance of the model.
[0069] The specific steps of optimizing the parameters of the LSTM network through the SSA algorithm and constructing the SSA-LSTM prediction model are as follows: Apply the optimal parameter configuration searched by the SSA algorithm to the LSTM model to construct the SSA-LSTM prediction model. These parameters include the learning rate, the number of hidden units, the number of iterations, etc.
[0070] It should be noted that LSTM is a special recurrent neural network that is specifically used to process and predict sequence data with long-term dependencies. The LSTM model consists of a series of LSTM modules, each of which contains three key gating structures: forget gate, input gate, and output gate. Figure 5 As shown, these gating structures decide whether to pass, discard, or update information through learned weights and activation functions.
[0071] The forget gate uses the Sigmoid activation function to convert the memory cells of the previous moment and the input of the current moment t into a value of 0 to 1, where 0 means discarding all information and 1 means retaining all information. This value determines how much information is retained. t The calculation formula is:
[0072] f t =σ(W f [h t-1 ,x t ]+b f ) (2)
[0073] Where σ is the Sigmoid function; W f is the weight matrix of the forget gate; h t-1 is the output neuron; x t is the input neuron at the current moment; b f is the forget gate bias term.
[0074] The input gate has two parts: the first part is the sigmoid layer, which determines the state of the updated memory cell; the second part is the tanh layer, which creates a new candidate memory cell state. t The calculation formula is:
[0075] I t =σ(W I [h t-1 ,x t]+b I ) (3)
[0076]
[0077] Where W I is the input gate weight matrix; b I 、b c are the bias terms of the input gate and the cell state respectively; is the alternative update information; tanh is the activation function; W c is the memory cell weight matrix; C t Memory cell state.
[0078] The output gate determines the output value of the unit state. The Sigmoid layer is used to determine which part is output, and then it is processed by tanh and multiplied by the output of the Sigmoid function. t The calculation formula is:
[0079] O t =σ(W o [h t-1 ,x t ]+b o ) (6)
[0080] h t =O t tanhC t (7)
[0081] Where W o is the output gate weight matrix; b o is the output gate bias term.
[0082] It should be noted that SSA simulates the strategies of sparrows in foraging and anti-predation behaviors, and has global search capabilities and good stability and convergence accuracy. The algorithm mainly consists of two parts: foraging stage and anti-predation stage.
[0083] In the foraging stage, sparrows discover potential food sources by observing the surrounding environment and exchanging information with other sparrows, and choose appropriate paths for exploration. Each sparrow searches for the optimal solution by updating its position based on its individual position and speed. In the anti-predation stage, sparrows need to protect themselves through rapid escape strategies when they encounter threats from predators. Sparrows update their positions based on their current positions and speeds to avoid predators and avoid falling into local optimal solutions. The position update calculation method in the SSA algorithm is as follows:
[0084]
[0085] Where t is the current iteration number, a ranges from {1,2,...,d}; X abis the b-th dimension position information of the a-th sparrow in the t-th iteration; iter max is the maximum number of iterations; α is a random number; S T is the safety domain value, which is a constant in the range of [0.5,1]; R2 is a random number, indicating the alarm value; Q is a random number that follows a normal distribution; L is a 1×d matrix. When R2<S T When R2>S T When , it indicates that the warning value is greater than the safety value, there is danger around, and all sparrows need to fly to a safe area quickly.
[0086] The speed update calculation of the sparrow is as follows:
[0087]
[0088] Where, X best is the current global optimal value; β is a random number that obeys the normal distribution, with a mean of 0 and a variance of 1, which is the step length control parameter; K is a random number, the positive or negative number indicates the direction of the sparrow's movement, and the size indicates the step length control parameter; f a is the fitness of the current individual; f u is the maximum fitness of the current individual; f w is the minimum fitness of the current individual; ε is the minimum constant and is not zero. a >f u When X is best is the location of the population center, and it is safe around it; when f a =f u When , it means that the sparrow is in the middle of the population and is aware of the danger. The sparrow must move closer to other individuals to avoid being attacked by predators; X h is the current location of the best sparrow finder; X worst is the worst individual in the current t-th iteration; A is a 1×d matrix, and its elements are randomly assigned 1 or -1.
[0089] Finally, by initializing the SSA-LSTM model and inputting voltage data for training, the mapping relationship between SiC MOSFET life and voltage is established to predict the remaining life of SiC MOSFET.
[0090] It should be noted that the parameters of the LSTM are initialized, including the number of neurons, weights, biases, the number of layers of the LSTM model, and the selection of the internal optimizer; the parameters of the SSA algorithm are initialized, including the size of the sparrow population, the ratio of predators (discoverers) to joiners, and the number of iterations of the algorithm.
[0091] It should be noted that the preprocessed voltage data is divided into a training set and a validation set. The constructed SSA-LSTM prediction model is trained using the training set data. According to the performance indicators on the validation set, the parameters of the SSA algorithm are continuously adjusted to improve the optimization process.
[0092] In each iteration, the SSA algorithm is used to optimize the parameters of the LSTM network. In each iteration, the SSA algorithm calculates the fitness value based on the position of each sparrow in the current population (i.e., the parameter combination of the LSTM model), and updates the position of the sparrow based on the fitness value to find a better LSTM model parameter combination. Repeat the above steps until the preset number of iterations is reached or the convergence condition is met, and the result of the remaining life of the SiC MOSFET is obtained. The SSA-LSTM prediction results are compared with the traditional LSTM prediction results, such as Figure 6 and Figure 7 shown.
[0093] according to Figure 6 and Figure 7 From the prediction results, it can be seen that the SSA-LSTM prediction method of this embodiment effectively improves the prediction accuracy in SiC MOSFET life prediction compared with the traditional LSTM prediction method.
[0094] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.
Claims
1. A SiC MOSFET life prediction method based on SSA-LSTM, characterized in that: include: The drain-source voltage is selected as the aging characteristic parameter of SiC MOSFET, and the voltage data is obtained in real time by using a sensor during the aging test; Preprocess the collected voltage data to eliminate outliers, supplement missing values, and smooth the data; The parameters of the long short-term memory network LSTM are optimized through the sparrow search algorithm SSA, and the SSA-LSTM prediction model is constructed; Initialize the SSA-LSTM model and input voltage data for training, establish the mapping relationship between SiC MOSFET life and voltage, and realize the prediction of the remaining life of SiC MOSFET.
2. The SiC MOSFET life prediction method based on SSA-LSTM according to claim 1, characterized in that: The method of using a sensor to obtain voltage data in real time during the aging test specifically includes: Based on the accelerated aging test of SiC MOSFET, according to the working principle and failure mechanism of SiC MOSFET, key voltage parameters closely related to its life are selected and collected through voltage sensors.
3. The SiC MOSFET life prediction method based on SSA-LSTM according to claim 1, characterized in that: The preprocessing of the collected voltage data specifically includes: The collected raw voltage data is cleaned, and outliers in the data are identified and eliminated using statistical methods. High-frequency noise is removed using filters, and interpolation is used to fill in the data, or records with missing values are deleted according to specific circumstances.
4. The SiC MOSFET life prediction method based on SSA-LSTM according to claim 1, characterized in that: The optimization of the parameters of the long short-term memory network LSTM by the sparrow search algorithm SSA specifically includes: The optimal parameter configuration searched by the SSA algorithm is applied to the LSTM model to build an SSA-LSTM prediction model. These parameters include learning rate, number of hidden units, number of iterations, etc.
5. The SiC MOSFET life prediction method based on SSA-LSTM according to claim 1, characterized in that: The initialization of the SSA-LSTM model specifically includes: Initializing the LSTM parameters, including the number of neurons, weights, biases, the number of layers of the LSTM model, and the selection of an internal optimizer; The initialized SSA algorithm parameters include the size of the sparrow population, the ratio of predators (discoverers) to joiners, and the number of iterations of the algorithm.
6. The SiC MOSFET life prediction method based on SSA-LSTM according to claim 1, characterized in that: The input voltage parameters are trained, specifically including: The preprocessed voltage data is divided into a training set and a validation set. The constructed SSA-LSTM prediction model is trained using the training set data. According to the performance indicators on the validation set, the parameters of the SSA algorithm are continuously adjusted to improve the optimization process.
7. The SiC MOSFET life prediction method based on SSA-LSTM according to claim 1, characterized in that: The prediction of the remaining life of SiC MOSFET specifically includes: In each iteration, the SSA algorithm is used to optimize the parameters of the LSTM network. In each iteration, the SSA algorithm calculates the fitness value based on the position of each sparrow in the current population (i.e., the parameter combination of the LSTM model), and updates the position of the sparrow based on the fitness value to find a better LSTM model parameter combination. Repeat the above steps until the preset number of iterations is reached or the convergence condition is met, and the result of the remaining life of the SiC MOSFET is obtained.
8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.
9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of claims 1 to 7 is implemented.