Method and device for evaluating health state of high-temperature aging test box

Abstract

The invention discloses a high-temperature aging test box health state assessment method and device. The method comprises the following steps: acquiring a nine-point temperature measurement signal, a compressor vibration signal and a fan rotation speed signal of a to-be-assessed high-temperature aging test box; performing feature extraction on the monitoring signal to obtain a multi-dimensional feature sequence; inputting the multi-dimensional feature sequence into a pre-trained time convolutional network model to obtain a degradation feature state of the high-temperature aging test box; and under the condition that the high-temperature aging test box is in a degradation state, determining a state degradation curve of the to-be-evaluated high-temperature aging test box based on the probability space point after the multi-dimensional feature sequence mapping and the health baseline in the time convolutional network model so as to determine the health state of the high-temperature aging test box. According to the method for evaluating the health state of the high-temperature aging test box, the health state of the high-temperature aging test box can be accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of non-destructive reliability screening of integrated circuits, and in particular to a method and device for evaluating the health status of a high-temperature aging test chamber. Background Art

[0002] Existing high-temperature aging products can achieve long-term monitoring of the test environment by adding long-term monitoring in the machine and overstress protection mechanisms, and can respond in time when the product fails, realizing post-maintenance based on fault data. However, due to the lack of active protection technology for high-temperature aging products, existing high-temperature aging products make it difficult for related products to achieve the integrity of the test process and the consistency of the test environment stress. It is very easy to cause the test process to be interrupted due to product failure, resulting in major property losses such as damage to millions of test devices (also known as test pieces), or the aging test is identified as a failure test due to adverse effects such as additional stress introduced during the test due to product performance degradation, resulting in ineffective waste of resources. At the same time, ensuring the quality of high-temperature aging tests can avoid over-aging and under-aging failures in integrated circuits, greatly reduce the failure rate of integrated circuits, and avoid large-scale electronic system failures and shutdowns such as new energy vehicles, civil airliners, and energy storage transmission stations due to integrated circuit failures.

[0003] High temperature aging products such as aging benches (also known as aging test benches) simulate various actual use environments and working conditions to make the product experience state changes similar to those after long-term use in a short period of time. Aging tests usually require the test equipment to be powered on for a long time to simulate the aging process of the product in actual use. The high temperature test chamber heats the air in the chamber to achieve a set high temperature environment in the test chamber, thereby realizing high temperature aging tests on the test piece.

[0004] High-temperature aging test chambers need to accurately create a high-temperature environment to simulate the product aging process in various scientific research and production tests. After long-term use, key components such as heating elements and temperature sensors may experience performance degradation, affecting the accuracy of the test results. In addition, if there is a problem with the air circulation system in the chamber during use, it will destroy the originally uniform temperature field, making the actual temperature of samples in different positions inconsistent, affecting the aging test. Real-time health assessment of the high-temperature aging test chamber can help testers to grasp the current status of the test chamber in a timely manner, discover potential hidden dangers in the high-temperature aging test chamber in advance, and repair or replace related components in advance to ensure that subsequent aging tests can be completed as planned.

[0005] It can be seen that there is an urgent need for technical personnel in this field to provide a high-temperature aging test chamber health status assessment solution. Summary of the invention

[0006] The purpose of the embodiments of the present invention is to provide a method and device for evaluating the health status of a high-temperature aging test chamber, and electronic equipment, which can solve the above-mentioned problems existing in the prior art.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: An embodiment of the present invention provides a method for evaluating the health status of a high temperature aging test chamber, wherein the method comprises: Collecting monitoring signals of the high-temperature aging test chamber to be evaluated, wherein the monitoring signals include: nine-point temperature measurement signals of the high-temperature aging test chamber, compressor vibration signals, and fan speed signals; Extracting features from the monitoring signal to obtain a multi-dimensional feature sequence; Inputting the multidimensional feature sequence into a pre-trained temporal convolutional network model to obtain the degradation feature state of the high temperature aging test chamber, wherein the degradation feature state includes: a healthy state and a degraded state; When the high temperature aging test chamber is in a degraded state, the state degradation curve of the high temperature aging test chamber to be evaluated is determined based on the probability space points after the multidimensional feature sequence is mapped and the health baseline in the temporal convolutional network model, wherein the state degradation curve can characterize the health state of the high temperature aging test chamber.

[0008] Optionally, the extracted features include at least two of the following: temperature fluctuation, temperature control accuracy, temperature uniformity, temperature overshoot, heating and cooling rates, vibration signal time and frequency domain features, and fan speed time domain features.

[0009] Optionally, the temporal convolutional network model is trained in the following manner: Obtaining a high temperature aging test chamber training time series degradation feature sample; wherein each training time series degradation feature sample is marked with a degradation feature state; Inputting the training temporal degradation feature sample into a pre-established temporal convolutional network model to obtain a mapped first probability space point; The degradation starting point of the high temperature aging test chamber is judged according to the two-dimensional first probability space point, and the health baseline of the high temperature aging test chamber is generated.

[0010] Optionally, the method further comprises: Obtain samples of timing degradation characteristics tested in a high-temperature aging test chamber; Inputting the test time series degradation feature sample into the temporal convolutional network model in training to obtain a mapped second probability space point; Based on a preset multi-distance metric algorithm, the distance between the second probability space and the health baseline is calculated.

[0011] Optionally, the step of calculating the distance between the second probability space and the health baseline based on a preset multi-distance metric algorithm includes: Calculating the distance between the second probability space and the health baseline based on the Mahalanobis distance algorithm, the cosine similarity algorithm, and the Manhattan distance algorithm, respectively, to obtain a first distance, a second distance, and a third distance; The first distance, the second distance and the third distance are weightedly summed to obtain the corresponding health degree.

[0012] The embodiment of the present invention further provides a high temperature aging test chamber health status assessment device, wherein the device comprises: The acquisition module is used to acquire the monitoring signals of the high temperature aging test chamber to be evaluated, wherein the monitoring signals include: the nine-point temperature measurement signals of the high temperature aging test chamber, the vibration signals of the compressor and the speed signals of the fan; An extraction module, used for extracting features from the monitoring signal to obtain a multi-dimensional feature sequence; A prediction module, used for inputting the multi-dimensional feature sequence into a pre-trained temporal convolutional network model to obtain the degradation feature state of the high temperature aging test chamber, wherein the degradation feature state includes: a healthy state and a degraded state; A determination module is used to determine, when the high-temperature aging test chamber is in a degraded state, a state degradation curve of the high-temperature aging test chamber to be evaluated based on the probability space points after mapping the multidimensional feature sequence and the health baseline in the time convolution network model, wherein the state degradation curve can characterize the health state of the high-temperature aging test chamber.

[0013] Optionally, the extracted features include at least two of the following: temperature fluctuation, temperature control accuracy, temperature uniformity, temperature overshoot, heating and cooling rates, vibration signal time and frequency domain features, and fan speed time domain features.

[0014] Optionally, the device further includes a model training module for training the temporal convolutional network model; the model training module includes: The first submodule is used to obtain the high temperature aging test chamber training time series degradation feature samples; wherein each training time series degradation feature sample is marked with a degradation feature state; The second submodule is used to input the training temporal degradation feature sample into a pre-established temporal convolutional network model to obtain a mapped first probability space point; The third submodule is used to determine the degradation starting point of the high temperature aging test chamber according to the two-dimensional first probability space point, and generate a health baseline of the high temperature aging test chamber.

[0015] Optionally, the model training module also includes: The fourth submodule is used to obtain the high temperature aging test chamber test timing degradation characteristic samples; A fifth submodule is used to input the test time series degradation feature sample into the time convolution network model in training to obtain a mapped second probability space point; The sixth submodule is used to calculate the distance between the second probability space and the health baseline based on a preset multi-distance metric algorithm.

[0016] Optionally, the sixth submodule is specifically used for: Calculating the distance between the second probability space and the health baseline based on the Mahalanobis distance algorithm, the cosine similarity algorithm, and the Manhattan distance algorithm, respectively, to obtain a first distance, a second distance, and a third distance; The first distance, the second distance and the third distance are weightedly summed to obtain the corresponding health degree.

[0017] An embodiment of the present invention also provides an electronic device, characterized in that it includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; the memory is used to store computer programs; the processor is used to implement any one of the above-mentioned high-temperature aging test chamber health status assessment method processes when executing the program stored in the memory.

[0018] The high temperature aging test chamber health status assessment scheme disclosed in the embodiment of the present invention collects monitoring signals such as nine-point temperature measurement signals, compressor vibration signals and fan speed signals of the high temperature aging test chamber to be evaluated; extracts features from the monitoring signals to obtain a multidimensional feature sequence; inputs the multidimensional feature sequence into a pre-trained time convolutional network model to obtain the degradation feature state of the high temperature aging test chamber; when the high temperature aging test chamber is in a degraded state, based on the probability space points mapped by the multidimensional feature sequence and the health baseline in the time convolutional network model, the state degradation curve of the high temperature aging test chamber to be evaluated is determined to determine the health state of the high temperature aging test chamber. The high temperature aging test chamber health status assessment scheme provided by the embodiment of the present invention collects the test chamber monitoring signals and extracts the multidimensional feature sequence, and predicts the multidimensional feature sequence based on the trained time convolutional network model, and can determine the state degradation curve of the high temperature aging test chamber to be evaluated, thereby accurately determining the health state of the high temperature aging test chamber. Since the health state of the high temperature aging test chamber can be accurately determined, it can be handled in time when health problems occur in the high temperature aging test chamber, thereby avoiding damage to the high temperature aging test chamber during the aging test of the test piece due to the failure of the high temperature aging test chamber. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flowchart showing a method for evaluating the health status of a high temperature aging test chamber according to an embodiment of the present application; Figure 2 is a flowchart showing a method for evaluating the health status of a high temperature aging test chamber according to an embodiment of the present application; Figure 3 is a flowchart showing the steps of a method for predicting the life of a switching power supply according to an embodiment of the present application; Figure 4 It is a structural block diagram of a high-temperature aging test chamber health status assessment device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0021] The following, in conjunction with the accompanying drawings, describes in detail the high-temperature aging test chamber health status assessment method provided in the embodiment of the present application through specific embodiments and their application scenarios.

[0022] As attached Figure 1 As shown, the high temperature aging test chamber health status assessment method of the embodiment of the present application includes the following steps: Step 101: Collect monitoring signals of the high temperature aging test chamber to be evaluated.

[0023] Among them, the monitoring signals include: nine-point temperature measurement signals of the high-temperature aging test chamber, compressor vibration signals and fan speed signals.

[0024] The high-temperature aging test chamber (also referred to as an aging high-temperature test chamber) health status assessment method provided by the present application can be applied to electronic devices. A high-temperature aging test chamber health status assessment computer program is provided in the electronic device. When the computer program is executed by the processor, the high-temperature aging test chamber health status assessment method of the present application embodiment is implemented.

[0025] The high-temperature aging provided in the present application can perform non-destructive aging testing on the test piece, and the high-temperature aging test chamber health status assessment method provided in many embodiments of the present application can detect the health assessment status of the high-temperature aging test chamber, thereby ensuring the safety of the test piece when the high-temperature aging is performed on the test piece.

[0026] In the actual implementation process, the temperature monitoring signal of the high-temperature aging test chamber directly measured has a large amount of data and contains a lot of redundant information. Therefore, preliminary feature extraction is performed on the directly measured data to obtain a feature sequence with high information density.

[0027] Step 102: Extract features from the monitoring signal to obtain a multi-dimensional feature sequence.

[0028] The extracted features include at least two of the following: temperature fluctuation, temperature control accuracy, temperature uniformity, temperature overshoot, heating and cooling rate, vibration signal time, frequency domain features, and fan speed time domain features, etc. The specific features extracted can be flexibly set by those skilled in the art according to actual needs, ensuring that the extracted features can characterize the state of the test box.

[0029] Step 103: Input the multidimensional feature sequence into the pre-trained temporal convolutional network model to obtain the degradation feature state of the high temperature and high aging test chamber.

[0030] Among them, the degradation characteristic state includes: healthy state and degraded state. If the degradation characteristic state of the high temperature aging test chamber is a healthy state, there is no need to execute step 104 and subsequent steps. If the degradation characteristic state of the high temperature aging test chamber is a degraded state, it means that the high temperature aging test chamber has been degraded. The degradation progress of the high temperature aging test chamber can be automatically identified by adaptively analyzing the multi-dimensional feature sequence data through the time convolution network model.

[0031] In an optional embodiment, the training process of the temporal convolutional network model may include the following sub-steps: Sub-step 1: Obtaining a sample of the timing degradation characteristics for training in a high-temperature aging test chamber; Each training time series degradation feature sample is marked with a degradation feature state. The high temperature aging test chamber training time series degradation feature sample can be obtained by monitoring the temperature signal and degradation feature state of a high temperature aging test chamber during its entire life cycle.

[0032] In the actual implementation process, the high temperature aging test chamber training time series degradation feature samples can be expressed as , the maximum number of iterations max_iter. Mark the degradation characteristic state corresponding to the high temperature aging test chamber for training , including healthy and degraded states.

[0033] Sub-step 2: input the training temporal degradation feature samples into the pre-established temporal convolutional network model to obtain the first probability space point after mapping; In the actual implementation process, For input, Train the TCN model (i.e., the temporal convolutional network model) for output; obtain the TCN model parameters obtained through iterative learning. When the number of iterations reaches the maximum number of iterations max_iter, stop iterative learning.

[0034] In the actual implementation process, the maximum number of iterations max_iter can be flexibly set by those skilled in the art, and no specific limitation is imposed on this in the embodiments of the present application.

[0035] Sub-step 3: Determine the degradation starting point of the high temperature aging test chamber according to the two-dimensional first probability space point, and generate a health baseline of the high temperature aging test chamber.

[0036] In an optional embodiment, the training process of the temporal convolutional network model may also include the following process: Acquire a sample of the test timing degradation feature of a high-temperature aging test chamber; input the test timing degradation feature sample into the time convolutional network model in training to obtain a mapped second probability space point; and calculate the distance between the second probability space and a healthy baseline based on a preset multi-distance metric algorithm.

[0037] The high temperature aging test chamber test timing degradation characteristic sample can be expressed as: In the training and testing process of the temporal convolutional network model, and Input the TCN algorithm model to obtain the mapped probability space points and .

[0038] The degradation starting point is automatically identified based on the two-dimensional probability space results, and the distance between the healthy space and the degradation space of the training and testing high-temperature aging test chamber is calculated using a weighted fusion multi-distance metric algorithm. Traverse all failure modes and obtain the health assessment results corresponding to each failure mode. The final trained temporal convolutional network model outputs and Health assessment curves corresponding to each high temperature aging test chamber.

[0039] More specifically, based on the preset multi-distance measurement algorithm, the distance between the second probability space and the health baseline can be calculated as follows: the distance between the second probability space and the health baseline is calculated based on the Mahalanobis distance algorithm, the cosine similarity algorithm and the Manhattan distance algorithm respectively to obtain the first distance, the second distance and the third distance; the first distance, the second distance and the third distance are weightedly summed to obtain the corresponding health level.

[0040] In the actual implementation process, it is not limited to using the above three distance algorithms to calculate the distance. Any one of the above three distance algorithms can also be used, or any two of the above three distance algorithms can be used to obtain two distances and then perform weighted summation of the two distances.

[0041] In the process of training the temporal convolutional network model, the high temperature aging test chamber test time series degradation feature samples are introduced, and the prediction accuracy of the temporal convolutional network model can be tested through the prediction results. In the actual implementation process, when the prediction accuracy of the temporal convolutional network model reaches the accuracy threshold, it can be determined that the training of this temporal convolutional network model is completed. After the subsequent training is completed, the temporal convolutional network model can be used to predict the state degradation curve of the high temperature aging test chamber to be evaluated.

[0042] Step 104: When the high temperature aging test chamber is in a degraded state, a state degradation curve of the high temperature aging test chamber to be evaluated is determined based on the probability space points after multi-dimensional feature sequence mapping and the health baseline in the time convolution network model.

[0043] Among them, the state degradation curve can characterize the health state of the high temperature aging test chamber.

[0044] By analyzing the multi-dimensional characteristics of the high-temperature aging test chamber to be evaluated through the time convolution network model, the health of the test chamber at the current moment can be obtained, and the state degradation curve up to that moment (also called the health degradation curve) can be output, providing reliable data support for further remaining service life prediction research of the high-temperature aging test chamber.

[0045] The high-temperature aging test chamber health status assessment method disclosed in the embodiment of the present invention collects monitoring signals of the high-temperature aging test chamber to be assessed; extracts features from the monitoring signals to obtain a multidimensional feature sequence; inputs the multidimensional feature sequence into a pre-trained temporal convolutional network model to obtain the degradation feature state of the high-temperature aging test chamber; when the high-temperature aging test chamber is in a degraded state, based on the probability space points after the multidimensional feature sequence is mapped and the health baseline in the temporal convolutional network model, the state degradation curve of the high-temperature aging test chamber to be assessed is determined to determine the health state of the high-temperature aging test chamber. The high-temperature aging test chamber health status assessment method provided in the embodiment of the present invention collects nine-point temperature measurement signals, compressor vibration signals, and fan speed signals of the test chamber and extracts a multidimensional feature sequence; the multidimensional feature sequence is predicted based on the trained temporal convolutional network model, and the state degradation curve of the high-temperature aging test chamber to be assessed can be determined, thereby accurately determining the health state of the high-temperature aging test chamber.

[0046] Combine the following Figure 2 The health status assessment scheme of the high temperature aging test chamber provided in this application is described with a specific example.

[0047] Provided in this specific example is a method for assessing the health status of a high-temperature aging test chamber based on time convolutional network degradation point identification and multi-distance metric fusion. The core is: during the aging test, the temperature of the high-temperature aging test chamber is monitored. The amount of data of the directly measured monitoring signal is large and contains a large amount of redundant information. First, preliminary feature extraction is performed on the directly measured data, including temperature fluctuation, temperature control accuracy, temperature uniformity, temperature overshoot, heating and cooling rates, vibration signal time and frequency domain characteristics, and fan speed time domain characteristics, etc., to obtain a feature sequence with high information density; the time convolutional network is used to adaptively analyze the feature sequence data to automatically identify the degradation starting point of the high-temperature aging test chamber. The distance metric algorithm is used to quantitatively calculate the health status of the feature sequence in the degradation stage to complete the health assessment of the high-temperature aging test chamber.

[0048] The process of the high temperature aging test chamber health status assessment method based on temporal convolutional network degradation point identification and multi-distance metric fusion can specifically include the following process: S1: High temperature aging test chamber temperature monitoring: High temperature aging test chamber Figure 2 The old temperature aging test chamber shown in . During the aging test, the nine-point temperature measurement signal, compressor vibration signal and fan speed signal of the high temperature aging test chamber are monitored by sensors to obtain large-scale monitoring data for the status evaluation of the test chamber.

[0049] S2: Preliminary feature extraction.

[0050] Since the amount of directly measured monitoring signal data is large and contains a lot of redundant information, it is necessary to first extract features from the direct monitoring data, including temperature fluctuation, temperature control accuracy, temperature uniformity, temperature overshoot, heating and cooling rates, vibration signal time and frequency domain characteristics, and fan speed time domain characteristics, etc., to obtain a feature sequence with high information density, namely the multi-dimensional degradation characteristics of the high temperature aging test chamber.

[0051] S3: Input the multi-dimensional degradation characteristics of the high-temperature aging test chamber into the temporal convolutional network model to automatically identify the degradation starting point and obtain the health baseline and degradation characteristics.

[0052] The health assessment process is further realized based on the extracted feature sequence: first, the TCN (Temporal Convolutional Network) model is used to identify the degradation starting point of the temperature data and feature data of the high-temperature aging test chamber, eliminating the instability caused by artificially setting a health baseline to construct health indicators. The TCN model is based on causal convolution, which strictly ensures that the output of each time step depends only on the input before the time step. Then, on this basis, dilated convolution is introduced to increase the receptive field according to specific rules by inserting intervals between the convolution kernel elements, so that it can effectively capture the long-term dependencies in the time series data. The TCN model reasonably stacks multiple modules containing causal convolution, dilated convolution, and residual connections, so that the time series data can extract rich and valuable feature information after layer-by-layer processing. Then, it is connected to the fully connected layer, which will integrate the previously extracted features and map them to a two-dimensional space to realize the identification of the degradation starting point of the high-temperature aging test chamber. Among them, Figure 2 The DilatedCausal Convolution is the dilated causal convolution, WeightNorm is the weight normalization layer, ReLU is the activation function, and Dropuot is the exit.

[0053] Specifically, during the training phase of the TCN model, training data with health status labels (i.e., training time series degradation feature samples and degradation feature state labels marked by each sample) are input. ,in is the feature sample, for The corresponding labels, 0 and 1, represent the healthy state and the degraded state respectively, and n is the number of feature samples obtained by sliding window resampling of the entire degradation curve. In this process, the TCN model continuously optimizes the mapping parameters of the input data to the low-dimensional feature space, strengthens the classification ability of the healthy state data and the degraded state data, and enables the TCN model to automatically segment the healthy baseline of the test data.

[0054] In the testing phase of the TCN model, the test data (i.e., the test chamber test timing degradation feature samples) are input. The TCN model can optimize the learned model parameters according to the training process, map the healthy baseline data and the degradation data into a two-dimensional probability space, and complete the identification of the degradation starting point of the test data. , avoiding the blindness of manually dividing the health baseline.

[0055] S4: A multi-distance metric fusion method is used to calculate the distance between the health baseline and the degradation characteristics, and the state degradation curve and the current health of the high-temperature aging test chamber are determined.

[0056] In order to further realize the health status assessment of the high-temperature aging test chamber at the current moment, the multi-distance metric fusion method is used to comprehensively consider the Mahalanobis distance, cosine similarity and Manhattan distance between sample data and perform weighted fusion to obtain the health status at the current moment. At the same time, the health degradation curve up to that moment is output, providing reliable data support for further remaining service life prediction research of the high-temperature aging test chamber.

[0057] The embodiment of the present application also provides a method for predicting the fault of the isolated switching power supply at the front end of the aging station considering multiple degradation paths. The method can be executed after the execution of the health status assessment process of the high-temperature aging test chamber is completed, or before the execution of the health status assessment process of the high-temperature aging test chamber, or in parallel with the process. The present application does not impose specific restrictions on the execution timing of the method for predicting the fault of the isolated switching power supply at the front end of the aging station considering multiple degradation paths.

[0058] As attached Figure 3 As shown, the method for predicting faults of isolated switching power supplies at the front end of a burn-in station considering multiple degradation paths in an embodiment of the present application includes the following steps: Step 301: performing dimensionality reduction processing on preset timing signals of multiple aging station switching power supplies, and extracting characteristic parameters.

[0059] The method for predicting the failure of the isolated switching power supply at the front end of the aging station considering multiple degradation paths provided by the implementation of the present application can be applied to electronic devices, and a computer program for predicting the life of the switching power supply at the aging station is provided in the electronic device. When the computer program is executed by the processor, the method for predicting the failure of the isolated switching power supply at the front end of the aging station considering multiple degradation paths of the embodiment of the present application is implemented to predict the remaining life of the switching power supply. The aging station switch in the embodiment of the present application is an isolated switch at the front end of the aging station, which can also be called a primary switching power supply.

[0060] The aging station provided in the embodiment of the present application can perform non-destructive aging testing on the parts to be tested. In the embodiment of the present application, the feature extraction and life prediction of the switching power supply of the aging station are to ensure the safety of the parts to be tested.

[0061] Among them, characteristic parameters include but are not limited to: ripple voltage, average output voltage, inductor current peak-to-peak value, inductor current peak value, capacitor current peak-to-peak value, capacitor current peak value, circuit board temperature, heat flux density, heat flow rate, thermal conductivity, thermal diffusivity, etc.

[0062] The preset timing signal can be a timing signal of the current, voltage, etc. of the switching power supply. The multiple aging station switching power supplies are multiple aging station switching power supplies with different decay rates and different fault initiation degrees.

[0063] Step 302: Calculate the similarity of the performance degradation paths of the switch power supplies of each aging station according to the characteristic parameters of each preset timing signal.

[0064] In the actual implementation process, the DTW (Dynamic Time Warping) method can be used to evaluate the similarity of the paths between the switching power supplies of multiple aging stations with different decay rates and different fault initiation degrees (that is, the similarity of the performance degradation paths of the switching power supplies of each aging station), and on this basis, the K-Means performance degradation path clustering method is introduced to achieve fast and accurate classification of the performance degradation paths of the switching power supplies of the aging station (that is, the aging test station).

[0065] Optionally, a method for calculating the similarity of the performance degradation paths of the switch power supplies of each aging station based on the characteristic parameters of each preset timing signal may include the following sub-steps: Sub-step 1: construct parameter vectors for the characteristic parameters of each preset time series signal respectively; Sub-step 2: constructing a matrix grid according to each of the parameter vectors; For the performance parameter vector of the primary switching power supply sample of the aging station , (a, b are sample numbers, m, n are corresponding running cycle numbers), then construct a The matrix grid, matrix elements express and Distance between two points , each matrix element express and Alignment.

[0066] Sub-step 3: Based on the preset path boundary constraints, path continuity constraints, and path monotonicity constraints, find the regular minimum cost path through the matrix grid through multiple iterative adjustments.

[0067] Among them, the regularized minimum cost path can characterize the similarity of the performance degradation paths of the switching power supplies of each aging station.

[0068] When implementing sub-step 3, a regular path through the grid can be found based on the path boundary constraints, path continuity constraints, and path monotonicity constraints, and then used To express: 1. Then, based on these three constraints, the path is iterated multiple times until the path with the minimum cost is obtained. , in the denominator Used to compensate for regular paths of different lengths.

[0069] Path boundary condition constraints can be set as , , ensuring that the selected path must start from the common starting point of the sequence and end at the common ending point of the sequence.

[0070] The path continuity constraint can be set as follows: , then for the next point on the path Needs to be satisfied and ,ensure and Each coordinate in Appeared in.

[0071] The path monotonicity constraint can be set as follows: , then for the next point on the path Needs to be satisfied and ,limit The above points must be monotonically progressing over time.

[0072] The DTW method is used to calculate the similarity of characteristic time series trajectories such as ripple voltage, average output voltage, inductor current peak-to-peak value, inductor current peak value, capacitor current peak-to-peak value, capacitor current peak value, circuit board temperature, heat flux density, heat flow rate, thermal conductivity, thermal diffusivity, etc. between different aging station switching power supply samples (i.e., preset timing signals). Subsequently, the clustering method can be used to cluster the time series trajectories, so that the aging station switching power supplies can be classified according to the performance degradation trajectory.

[0073] Step 303: clustering each degradation path according to the similarity of the performance degradation paths of the switch power supplies of each aging station to obtain N degradation path sets.

[0074] N is a positive integer greater than or equal to 2. In the embodiment of the present application, N equals 2 as an example for illustration.

[0075] In an optional embodiment, clustering each degradation path according to the similarity of the performance degradation paths of the switch power supplies of each aging station to obtain N degradation path sets may include the following sub-steps: Sub-step 1: Randomly select K points from the regular minimum cost path as the centers of the initial cluster; Sub-step 2: For each point in the regularized minimum cost path, assign each point to the cluster closest to the center point; Sub-step 3: Update the center point of each cluster; An exemplary method of updating the center point of each cluster may be: for each cluster, calculating the average value of each point in the cluster; and updating the center point of the cluster to the location of the average value. The goal of updating the center point of the cluster is to minimize the squared error within the cluster.

[0076] Sub-step 4: For each point in the regularized minimum cost path, assign each point to the cluster closest to the updated center point; Sub-step 5: Determine whether the clustering stop condition is met. If not, return to sub-step 3 to update the center point of each cluster. If not reached, return to execute sub-step 3, sub-step 4 and sub-step 5, so as to update the center point again and cluster based on the updated center point.

[0077] The clustering stop condition can be flexibly set by those skilled in the art, and is not specifically limited in the embodiments of the present application. For example, the clustering stop condition can be set to: the change in the position of the cluster center point before and after the update is less than a preset value (the position of the cluster center point before and after the update no longer changes significantly), or it can be set to reach a set upper limit of the number of iterations.

[0078] This optional degradation path clustering method takes advantage of the time used for cluster centroid selection and the complexity of cluster overlapping space in K-Means, and takes the optimal point that actually exists in the data set as its centroid. Therefore, the characteristics of the trajectory corresponding to the centroid can be found, and the system samples are clustered through the performance degradation path similarity matrix of the aging station switching power supply, so as to realize the identification of the aging station switching power supply according to the performance degradation trajectory time series (i.e., the preset timing signal).

[0079] Sub-step 6: If it has been reached, the clusters of the updated center points are used as the N degenerate path sets.

[0080] Step 304: Establish N switch power supply life prediction models, and train the N switch power supply life prediction models according to the N degradation path sets respectively.

[0081] Among them, the switching power supply life prediction model includes: Input layer, Dense layer (i.e. dense layer), BiLSTM layer (i.e. bidirectional long short-term memory network layer), used to process multi-sensor monitoring signals, BiGRU layer (i.e. bidirectional gated recurrent layer), used to process operation condition data (i.e. transformation results after hidden representation), Concatenate layer (i.e. feature concatenation layer), used to fuse the outputs of BiLSTM and BiGRU layers, multi-dimensional feature maps, and output layer, used to output RUL prediction values.

[0082] In an optional embodiment, establishing N switch power supply life prediction models, and training the N switch power supply life prediction models according to N degradation path sets respectively may include the following sub-steps: Sub-step 1: Establish N switching power supply life prediction models.

[0083] When there are two performance degradation paths, two switching power supply life prediction models are established, and the switching power supply life prediction models are used to predict the remaining life of the switching power supply.

[0084] Sub-step 2: For each switching power supply life prediction model, the characteristic parameters corresponding to the degradation path in the corresponding degradation path set are used as model input.

[0085] Sub-step 3: Perform linear transformation on the input feature parameters in the fully connected layer and hide the transformation results; Among them, the input feature parameters can be regarded as multi-sensory monitoring data X i .

[0086] Sub-step 4: Construct a high-order vector based on the transformation result after the hidden representation, and send the high-order vector to the BiLSTM layer and BiGRU layer to obtain hidden feature maps of different dimensions; Among them, the number of stacked layers of BiLSTM layer and BiGRU layer is the same.

[0087] Sub-step 5: The feature vectors generated by merging the hidden features of different dimensions are input into two linear regression dense layers to generate RUL prediction values; Sub-step 6: Calculate the loss by using the error between the RUL prediction value and the RUL actual value as the loss function; Sub-step 7: updating the parameters of the switching power supply life prediction model according to the loss back propagation; Sub-step 8: Determine whether the switching power supply life prediction model after the updated parameters meets the preset accuracy; if so, determine that the training of the switching power supply life prediction model is completed; if not, return to the step of using the characteristic parameters corresponding to the degradation path in the corresponding degradation path set as the model input.

[0088] The prediction accuracy is used to evaluate the performance of the prediction model. The accuracy of the switching power supply life prediction model can be calculated using the following formula:

[0089] Among them, RUL is the actual RUL (remaining useful life); is the predicted RUL estimated by the prediction model; TUL is the actual total service life of the target circuit, which is defined as the total number of test cycles when the switching power supply of the aging station reaches the failure threshold.

[0090] The life prediction model of the switching power supply can be a multidimensional recurrent neural network (MDRNN) model. The multidimensional recurrent neural network uses momentum and adaptive learning rate to accelerate the convergence speed, and is proven to be superior to the classic stochastic gradient descent optimizer, thereby obtaining higher processing efficiency and prediction quality. The training process of the multidimensional recurrent neural network (MDRNN) model includes: S1: extracting the characteristic parameters of the switching power supply of the aging station; S2: establishing the MDRNN multidimensional recurrent neural network model; S3: initializing the parameters of the MDRNN multidimensional recurrent neural network model; S4: iteratively training the MDRNN multidimensional recurrent neural network model based on the characteristic parameters in S1 for multiple times, and calculating the loss function after each training is completed, and adjusting the model parameters based on the calculated loss function; after multiple rounds of forward and back propagation training, after reaching the stop state, the trained MDRNN multidimensional recurrent neural network model can be used to predict the remaining life of the switching power supply.

[0091] Step 305: Based on the trained N switch power supply life prediction models, the life of the switch power supply of the aging station is predicted to obtain the remaining life of the switch power supply.

[0092] When predicting the life of the switching power supply of the aging station based on N switching power supply life prediction models, the degradation path category can be determined based on the timing signal of the switching power supply to be predicted, and the switching power supply life prediction model matching the category can be called to make an accurate prediction. The specific prediction process may include: determining the characteristic parameters of the timing signal of the switching power supply of the aging station to be predicted; determining the degradation category to which the switching power supply of the aging station to be predicted belongs; inputting the characteristic parameters into the switching power supply life prediction model of the matching category to predict the remaining service life of the switching power supply. The prediction results of this time can also be used as basic data for the accuracy of the subsequent evaluation results of the switch unit prediction model, specifically: calculating the prediction error of the model based on the remaining service life obtained from this prediction and the actual data of the remaining service life of the switching power supply; and evaluating the accuracy of the model prediction results based on the prediction error.

[0093] The embodiment of the present application provides a method for predicting faults of isolated switching power supplies at the front end of an aging station considering multiple degradation paths, which performs dimensionality reduction processing on preset timing signals of multiple switching power supplies of the aging station and extracts characteristic parameters; calculates the similarity of the performance degradation paths of the switching power supplies of each aging station based on the characteristic parameters of each preset timing signal; clusters each degradation path based on the similarity of the performance degradation paths of the switching power supplies of each aging station to obtain N degradation path sets; establishes N switching power supply life prediction models, and trains the N switching power supply life prediction models based on the N degradation path sets respectively; predicts the life of the switching power supply of the aging station based on the trained N switching power supply life prediction models to obtain the remaining life of the switching power supply. The method for predicting faults of isolated switching power supplies at the front end of an aging station considering multiple degradation paths has, on the one hand, a life prediction model of the switching power supply of the aging station based on a multidimensional recurrent neural network model comprehensively considering the impact of performance degradation paths on the prediction of the remaining service life of the switching power supply, and can achieve high-precision life prediction; on the other hand, the failure mode is taken into consideration to identify the performance degradation path of the switching power supply of the aging station, and the characteristic parameters of the switching power supply of the aging station with the same performance degradation path are brought into the same model for training and prediction, which is beneficial to improving the accuracy of life prediction of the switching power supply of the aging station.

[0094] Figure 4 A structural block diagram of a high-temperature aging test chamber health status assessment device for implementing an embodiment of the present application.

[0095] The high temperature aging test chamber health status assessment device provided in the embodiment of the present application includes the following functional modules: The acquisition module 401 is used to acquire monitoring signals of the high temperature aging test chamber to be evaluated, wherein the monitoring signals include: nine-point temperature measurement signals of the high temperature aging test chamber, compressor vibration signals and fan speed signals; An extraction module 402 is used to extract features from the monitoring signal to obtain a multi-dimensional feature sequence; The prediction module 403 is used to input the multi-dimensional feature sequence into a pre-trained temporal convolutional network model to obtain the degradation feature state of the high temperature aging test chamber, wherein the degradation feature state includes: a healthy state and a degraded state; The determination module 404 is used to determine the state degradation curve of the high-temperature aging test chamber to be evaluated based on the probability space points after the multi-dimensional feature sequence mapping and the health baseline in the time convolution network model when the high-temperature aging test chamber is in a degraded state, wherein the state degradation curve can characterize the health state of the high-temperature aging test chamber.

[0096] Optionally, the device further includes a model training module for training the temporal convolutional network model; the model training module includes: The first submodule is used to obtain a high temperature aging test chamber training time series degradation characteristic sample; wherein each training time series degradation characteristic sample is marked with a degradation characteristic state; The second submodule is used to input the training temporal degradation feature sample into a pre-established temporal convolutional network model to obtain a mapped first probability space point; The third submodule is used to determine the degradation starting point of the high temperature aging test chamber according to the two-dimensional first probability space point, and generate a health baseline of the high temperature aging test chamber.

[0097] Optionally, the extracted features include at least two of the following: temperature fluctuation, temperature control accuracy, temperature uniformity, temperature overshoot, heating and cooling rates, vibration signal time domain features, vibration signal frequency domain features, and fan speed time domain features.

[0098] Optionally, the model training module also includes: The fourth submodule is used to obtain the timing degradation characteristic samples of the high temperature aging test chamber test; A fifth submodule is used to input the test time series degradation feature sample into the time convolution network model in training to obtain a mapped second probability space point; The sixth submodule is used to calculate the distance between the second probability space and the health baseline based on a preset multi-distance metric algorithm.

[0099] Optionally, the sixth submodule is specifically used for: Calculating the distance between the second probability space and the health baseline based on the Mahalanobis distance algorithm, the cosine similarity algorithm, and the Manhattan distance algorithm, respectively, to obtain a first distance, a second distance, and a third distance; The first distance, the second distance and the third distance are weightedly summed to obtain the corresponding health degree.

[0100] The embodiments of the present application provide Figure 4 The high temperature aging test chamber health status assessment device shown can achieve Figure 1 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0101] The high-temperature aging test chamber health status assessment device provided in the embodiment of the present application collects monitoring signals such as the nine-point temperature measurement signal, the compressor vibration signal, and the fan speed signal of the high-temperature aging test chamber to be evaluated; performs feature extraction on the monitoring signal to obtain a multidimensional feature sequence; inputs the multidimensional feature sequence into a pre-trained time convolutional network model to obtain the degradation feature state of the high-temperature aging test chamber; when the high-temperature aging test chamber is in a degraded state, based on the probability space points after the multidimensional feature sequence is mapped and the health baseline in the time convolutional network model, the state degradation curve of the high-temperature aging test chamber to be evaluated is determined to determine the health state of the high-temperature aging test chamber. The high-temperature aging test chamber health status assessment device provided in the embodiment of the present application collects the test chamber monitoring signal and extracts the multidimensional feature sequence, and predicts the multidimensional feature sequence based on the trained time convolutional network model, and can determine the state degradation curve of the high-temperature aging test chamber to be evaluated, thereby accurately determining the health state of the high-temperature aging test chamber.

[0102] An embodiment of the present invention further provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus.

[0103] Memory, used to store computer programs; The processor is used to implement the high temperature aging test chamber health status assessment method shown in the above method embodiment when executing the program stored in the memory.

[0104] The communication bus mentioned in the above terminal may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus may be divided into an address bus, a data bus, a control bus, etc.

[0105] The communication interface is used for communication between the above terminal and other devices.

[0106] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0107] In another embodiment provided by the present invention, a computer-readable storage medium is also provided, which stores instructions. When the computer-readable storage medium is executed on an electronic device, the electronic device implements the high-temperature aging test chamber health status assessment method described in any of the above embodiments.

[0108] In another embodiment provided by the present invention, a computer program product containing instructions is also provided. When the computer program product is run on an electronic device, the electronic device implements the high-temperature aging test chamber health status assessment method described in any of the above embodiments.

[0109] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0110] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for evaluating the health status of a high temperature aging test chamber, characterized in that: The method comprises: Collect monitoring signals of the high-temperature aging test chamber to be evaluated; wherein the monitoring signals include: nine-point temperature measurement signals of the high-temperature aging test chamber, compressor vibration signals, and fan speed signals; Extracting features from the monitoring signal to obtain a multi-dimensional feature sequence; Inputting the multidimensional feature sequence into a pre-trained temporal convolutional network model to obtain the degradation feature state of the high temperature aging test chamber, wherein the degradation feature state includes: a healthy state and a degraded state; When the high temperature aging test chamber is in a degraded state, the state degradation curve of the high temperature aging test chamber to be evaluated is determined based on the probability space points after the multidimensional feature sequence is mapped and the health baseline in the temporal convolutional network model, wherein the state degradation curve can characterize the health state of the high temperature aging test chamber.

2. The method according to claim 1, characterized in that: in, The extracted features include at least two of the following: temperature fluctuation, temperature control accuracy, temperature uniformity, temperature overshoot, heating and cooling rate, vibration signal time domain features, vibration signal frequency domain features, and fan speed time domain features.

3. The method according to claim 1, characterized in that The temporal convolutional network model is trained in the following way: Obtaining a high temperature aging test chamber training time series degradation feature sample; wherein each training time series degradation feature sample is marked with a degradation feature state; Inputting the training temporal degradation feature sample into a pre-established temporal convolutional network model to obtain a mapped first probability space point; The degradation starting point of the high temperature aging test chamber is judged according to the two-dimensional first probability space point, and the health baseline of the high temperature aging test chamber is generated.

4. The method according to claim 3, characterized in that The method further comprises: Obtain samples of timing degradation characteristics tested in a high-temperature aging test chamber; Inputting the test time series degradation feature sample into the temporal convolutional network model in training to obtain a mapped second probability space point; Based on a preset multi-distance metric algorithm, the distance between the second probability space and the health baseline is calculated.

5. The method according to claim 4, characterized in that The step of calculating the distance between the second probability space and the health baseline based on a preset multi-distance metric algorithm includes: Calculating the distance between the second probability space and the health baseline based on the Mahalanobis distance algorithm, the cosine similarity algorithm, and the Manhattan distance algorithm, respectively, to obtain a first distance, a second distance, and a third distance; The first distance, the second distance and the third distance are weightedly summed to obtain the corresponding health degree.

6. A high temperature aging test chamber health status assessment device, characterized in that: The device comprises: The acquisition module is used to acquire the monitoring signal of the high temperature aging test chamber to be evaluated; wherein the monitoring signal includes: the nine-point temperature measurement signal of the high temperature aging test chamber, the compressor vibration signal and the fan speed signal; An extraction module, used for extracting features from the monitoring signal to obtain a multi-dimensional feature sequence; A prediction module, used for inputting the multi-dimensional feature sequence into a pre-trained temporal convolutional network model to obtain the degradation feature state of the high temperature aging test chamber, wherein the degradation feature state includes: a healthy state and a degraded state; A determination module is used to determine, when the high-temperature aging test chamber is in a degraded state, a state degradation curve of the high-temperature aging test chamber to be evaluated based on the probability space points after mapping the multidimensional feature sequence and the health baseline in the time convolution network model, wherein the state degradation curve can characterize the health state of the high-temperature aging test chamber.

7. The device according to claim 6, characterized in that The device also includes a model training module for training the temporal convolutional network model; the model training module includes: The first submodule is used to obtain the high temperature aging test chamber training time series degradation feature samples; wherein each training time series degradation feature sample is marked with a degradation feature state; The second submodule is used to input the training temporal degradation feature sample into a pre-established temporal convolutional network model to obtain a mapped first probability space point; The third submodule is used to determine the degradation starting point of the high temperature aging test chamber according to the two-dimensional first probability space point, and generate a health baseline of the high temperature aging test chamber.

8. The device according to claim 7, characterized in that The model training module also includes: The fourth submodule is used to obtain the timing degradation characteristic samples of the high temperature aging test chamber test; A fifth submodule is used to input the test time series degradation feature sample into the time convolution network model in training to obtain a mapped second probability space point; The sixth submodule is used to calculate the distance between the second probability space and the health baseline based on a preset multi-distance metric algorithm.

9. The device according to claim 8, characterized in that The sixth submodule is specifically used for: Calculating the distance between the second probability space and the health baseline based on the Mahalanobis distance algorithm, the cosine similarity algorithm, and the Manhattan distance algorithm, respectively, to obtain a first distance, a second distance, and a third distance; The first distance, the second distance and the third distance are weightedly summed to obtain the corresponding health degree.

10. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the high temperature aging test chamber health status assessment method as described in any one of claims 1 to 5 when executing the program stored in the memory.

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