Method and device for evaluating health state of secondary power supply of high-temperature aging test equipment

Abstract

The invention discloses a method and a device for evaluating the health state of a secondary power supply of high-temperature aging test equipment. The method comprises the following steps: collecting a secondary power supply monitoring signal of the to-be-evaluated high-temperature aging test equipment; carrying out feature extraction on current and voltage output monitoring signals, and mixing a power supply ripple fluctuation rate, a degeneration curve from an MOSFET shell temperature to a junction temperature and the like to obtain a multi-dimensional feature sequence; inputting the multi-dimensional feature sequence into a pre-trained residual convolutional neural network model to obtain a degradation feature state of the secondary power supply of the high-temperature aging test equipment; and under the condition that the secondary power supply of the high-temperature aging test equipment is in a degradation state, determining a state degradation curve of the secondary power supply of the to-be-evaluated high-temperature aging test equipment based on the probability space points after the multi-dimensional feature sequence mapping and the health baseline in the residual convolutional neural network model so as to determine the health state of the secondary power supply of the high-temperature aging test equipment.

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 secondary power supply of a high-temperature aging test device. 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 equipment) simulate various actual use environments and working conditions to make the product undergo 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 secondary power supply refers to the power supply device that converts and distributes the primary power supply in the high temperature aging test bench to meet the specific power requirements required by different loads (such as electronic components, circuit boards, etc.) in the aging test.

[0004] During the aging test, if the secondary power supply fails, such as the output voltage suddenly drops or disappears, the aging test will be unexpectedly interrupted. During the use of the aging test equipment, problems such as capacitor aging and switch tube performance degradation will gradually occur. By conducting a health assessment of the secondary power supply of the aging test equipment, potential hidden dangers inside the power supply can be discovered in advance, and repairs or replacements can be made in time to avoid test interruptions and ensure that the aging test can proceed smoothly as planned.

[0005] It can be seen that there is an urgent need for technical personnel in this field to provide a secondary power supply health status assessment solution for high-temperature aging test equipment. 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 secondary power supply of a high-temperature aging test device, and an electronic device, 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 secondary power supply of a high-temperature aging test device, wherein the method comprises: Collect the secondary power supply monitoring signals of the high-temperature aging test equipment to be evaluated, including current signals, voltage output signals, power supply ripple fluctuation rate, MOSFET device case temperature to junction temperature, etc.; Feature extraction is performed on monitoring signals such as current and voltage output, and multi-dimensional feature sequences are obtained by mixing power supply ripple fluctuation rate and MOSFET case temperature to junction temperature degradation curve; Inputting the multidimensional feature sequence into a pre-trained residual convolutional neural network model to obtain the degradation feature state of the secondary power supply of the high temperature aging test equipment, wherein the degradation feature state includes: a healthy state and a degraded state; When the secondary power supply of the high-temperature aging test equipment is in a degraded state, the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated is determined based on the probability space points after mapping the multi-dimensional feature sequence and the health baseline in the residual convolutional neural network model, wherein the state degradation curve can characterize the health state of the secondary power supply of the high-temperature aging test equipment.

[0008] Optionally, the residual convolutional neural network model includes: a convolution layer, a batch normalization layer, an activation function, a maximum pooling layer, multiple residual blocks, a global average pooling layer and a fully connected layer.

[0009] Optionally, the residual convolutional neural network model is trained in the following manner: Acquire training timing degradation feature samples of secondary power supply of high temperature aging test equipment; wherein each training timing degradation feature sample is marked with a degradation feature state; Inputting the training time series degradation feature samples into a pre-established residual convolutional neural network model to obtain a mapped first probability space point; The degradation starting point of the secondary power supply of the high-temperature aging test equipment is determined according to the two-dimensional first probability space point, and a health baseline of the secondary power supply of the high-temperature aging test equipment is generated.

[0010] Optionally, the method further comprises: Obtain the timing degradation characteristic samples of the secondary power supply test of the high temperature aging test equipment; Inputting the test time series degradation feature sample into the residual convolutional neural 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 health of the second power supply of the high temperature aging test equipment.

[0012] The embodiment of the present invention further provides a secondary power supply health status assessment device for high temperature aging test equipment, wherein the device comprises: The acquisition module is used to collect the secondary power supply monitoring signal of the high-temperature aging test equipment to be evaluated; wherein the monitoring signal includes: current signal, voltage output signal, power supply ripple fluctuation rate, MOSFET shell temperature to junction temperature; An extraction module, used for extracting features from the current signal and the voltage output signal, and then mixing the power supply ripple fluctuation rate and the MOSFET shell temperature to junction temperature degradation curve to obtain a multi-dimensional feature sequence; A prediction module, used for inputting the multi-dimensional feature sequence into a pre-trained residual convolutional neural network model to obtain a degradation characteristic state of the secondary power supply of the high temperature aging test equipment, wherein the degradation characteristic state includes: a healthy state and a degraded state; A determination module is used to determine, when the secondary power supply of the high-temperature aging test equipment is in a degraded state, the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated based on the probability space points after mapping the multi-dimensional feature sequence and the health baseline in the residual convolutional neural network model, wherein the state degradation curve can characterize the health state of the secondary power supply of the high-temperature aging test equipment.

[0013] Optionally, the residual convolutional neural network model includes: a convolution layer, a batch normalization layer, an activation function, a maximum pooling layer, multiple residual blocks, a global average pooling layer and a fully connected layer.

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

[0015] Optionally, the training module further includes: The fourth submodule is used to obtain the timing degradation characteristic samples of the secondary power supply test of the high temperature aging test equipment; A fifth submodule is used to input the test time series degradation feature sample into the residual convolutional neural 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 health of the second power supply of the high temperature aging test equipment.

[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 equipment secondary power supply health status assessment methods when executing the program stored in the memory.

[0018] The high-temperature aging test equipment secondary power supply health status assessment scheme disclosed in the embodiment of the present invention collects the secondary power supply monitoring signal of the high-temperature aging test equipment to be evaluated; obtains a multidimensional feature sequence based on the monitoring signal; inputs the multidimensional feature sequence into a pre-trained residual convolutional neural network model to obtain the degradation feature state of the secondary power supply of the high-temperature aging test equipment; when the secondary power supply of the high-temperature aging test equipment is in a degraded state, the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated is determined based on the probability space points mapped by the multidimensional feature sequence and the health baseline in the residual convolutional neural network model, thereby accurately determining the health state of the secondary power supply of the high-temperature aging test equipment. The high-temperature aging test equipment secondary power supply health status assessment scheme provided by the embodiment of the present invention collects the secondary power supply monitoring signal and extracts the multidimensional feature sequence, and predicts the multidimensional feature sequence based on the trained model to obtain the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated, thereby accurately determining the health state of the secondary power supply of the high-temperature aging test equipment. Since the health state of the secondary power supply of the high-temperature test equipment can be accurately determined, it can effectively avoid the damage of the test piece to be tested due to the secondary power supply failure during the aging test of the test piece by the high-temperature test equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flowchart showing the steps of a method for evaluating the health status of a secondary power supply of a high temperature aging test equipment according to an embodiment of the present application; Figure 2 is a flowchart showing the steps of a method for evaluating the health status of a secondary power supply of a high temperature aging test equipment according to an embodiment of the present application; Figure 3 It is a flowchart showing the steps of a method for deep fusion of time-frequency characteristics of a secondary power supply of a high-temperature aging test equipment according to an embodiment of the present application; Figure 4 It is a structural block diagram of a secondary power supply health status assessment device for high-temperature aging test equipment 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 method for evaluating the health status of a secondary power supply of a high-temperature aging test equipment provided in the embodiment of the present application through specific embodiments and their application scenarios.

[0022] As attached Figure 1 As shown, the method for evaluating the health status of secondary power supply of high temperature aging test equipment in the embodiment of the present application includes the following steps: Step 101: Collect the secondary power supply monitoring signal of the high temperature aging test equipment to be evaluated.

[0023] The method for evaluating the health status of a secondary power supply of a high-temperature aging test equipment provided in the implementation of the present application can be applied to electronic devices. A computer program for evaluating the health status of a secondary power supply of a high-temperature aging test equipment is provided in the electronic device. When the computer program is executed by a processor, the method for evaluating the health status of a secondary power supply of a high-temperature aging test equipment in the embodiment of the present application is implemented.

[0024] The aging test equipment provided in the present application can perform non-destructive aging testing on the test piece. The secondary power supply health status assessment method made in the embodiment of the present application can assess the health status of the secondary power supply of the aging test equipment. The health assessment of the secondary power supply of the aging station can ensure the safety of the test piece.

[0025] In the actual implementation process, the data volume of the secondary power supply monitoring signal of the high-temperature aging test equipment directly measured is large 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.

[0026] Step 102: Generate a multi-dimensional feature sequence according to the monitoring signal.

[0027] The monitoring signals include but are not limited to current signals and voltage output signals, power supply ripple fluctuation rate, MOSFET shell temperature to junction temperature, etc. The specific features extracted can be flexibly set by technicians in this field according to actual needs to ensure that the extracted features can characterize the state of the secondary power supply. Among them, MOSFET is abbreviated as MOS, which is a metal oxide semiconductor effect transistor and a voltage-controlled semiconductor device. In this embodiment, it refers to the voltage-controlled semiconductor device in the secondary power supply of the high temperature aging test equipment.

[0028] Specifically, the features of the current signal and the voltage output signal are extracted, and then the power supply ripple fluctuation rate and the degradation curve from the case temperature to the junction temperature of the MOSFET device are mixed to obtain a multi-dimensional feature sequence.

[0029] Step 103: Input the multidimensional feature sequence into a pre-trained residual convolutional neural network model to obtain the degradation characteristic state of the secondary power supply of the high temperature aging test equipment.

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

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

[0032] In the actual implementation process, the secondary power supply training timing degradation feature sample of the high temperature aging test equipment can be expressed as , the maximum number of iterations max_iter. Mark the degradation characteristic state corresponding to the secondary power supply of the high temperature aging test equipment for training , including healthy and degraded states.

[0033] Sub-step 2: Input the training time series degradation feature samples into the pre-established residual convolutional neural network model to obtain the first probability space point after mapping; The residual convolutional neural network model includes: convolution layer, batch normalization layer, activation function (ReLU), maximum pooling layer, multiple residual blocks, global average pooling layer and fully connected layer.

[0034] In the actual implementation process, For input, Train the ResNet model (i.e., residual convolutional neural network model) for output; obtain iterative learning to update the ResNet model parameters. When the number of iterations reaches the maximum number of iterations max_iter, stop iterative learning.

[0035] 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.

[0036] Sub-step 3: Determine the starting point of secondary power degradation of the high-temperature aging test equipment based on the two-dimensional first probability space point, and generate a secondary power health baseline for the high-temperature aging test equipment.

[0037] In an optional embodiment, the training process of the residual convolutional neural network model may also include the following process: Obtain test timing degradation feature samples of the secondary power supply of the high-temperature aging test equipment; input the test timing degradation feature samples into the residual convolutional neural network model in training to obtain the mapped second probability space points; and calculate the distance between the second probability space and the healthy baseline based on a preset multi-distance metric algorithm.

[0038] The timing degradation characteristic sample of the secondary power supply test of high temperature aging test equipment can be expressed as: In the training and testing process of the residual convolutional neural network model, and Input the ResNet algorithm model to obtain the mapped probability space points and .

[0039] The degradation starting point is automatically identified based on the two-dimensional probability space results, and the weighted fusion multi-distance metric algorithm is used to calculate the distance between the healthy space and the degraded space of the secondary power supply of the high-temperature aging test equipment for training and testing. . Traverse all fault modes and obtain the health assessment results corresponding to each fault mode. The final trained residual convolutional neural network model outputs and Health assessment curve corresponding to the second power supply of each high temperature aging test equipment.

[0040] 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.

[0041] 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.

[0042] In the training process of the residual convolutional neural network model, the test timing degradation feature samples of the secondary power supply of the high-temperature aging test equipment are introduced, and the prediction accuracy of the residual convolutional neural network model can be tested through the prediction results. In the actual implementation process, when the prediction accuracy of the residual convolutional neural network model reaches the accuracy threshold, it can be determined that the training of this residual convolutional neural network model is completed. After the subsequent training is completed, the residual convolutional neural network model can be used to predict the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated.

[0043] Step 104: When the secondary power supply of the high temperature aging test equipment is in a degraded state, the state degradation curve of the secondary power supply of the high temperature aging test equipment to be evaluated is determined based on the probability space points after multi-dimensional feature sequence mapping and the health baseline in the residual convolutional neural network model.

[0044] Among them, the state degradation curve can characterize the health status of the secondary power supply of the high temperature aging test equipment.

[0045] By analyzing the multi-dimensional characteristics of the secondary power supply of the high-temperature aging test equipment to be evaluated through the residual convolutional neural network model, the health of the secondary power supply 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 secondary power supply of the high-temperature aging test equipment.

[0046] The method for evaluating the health status of the secondary power supply of the high-temperature aging test equipment disclosed in the embodiment of the present invention collects the monitoring signal of the secondary power supply of the high-temperature aging test equipment to be evaluated, generates a multidimensional feature sequence according to the monitoring signal; inputs the multidimensional feature sequence into a pre-trained residual convolutional neural network model to obtain the degradation feature state of the secondary power supply of the high-temperature aging test equipment; when the secondary power supply of the high-temperature aging test equipment is in a degraded state, based on the probability space points mapped by the multidimensional feature sequence and the health baseline in the residual convolutional neural network model, the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated is determined, thereby accurately determining the health status of the secondary power supply of the high-temperature aging test equipment. The health status evaluation scheme for the secondary power supply of the high-temperature aging test equipment provided in the embodiment of the present invention collects the monitoring signal of the secondary power supply and extracts the multidimensional feature sequence, and predicts the multidimensional feature sequence based on the trained model to obtain the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated, thereby accurately determining the health status of the secondary power supply of the high-temperature aging test equipment.

[0047] Combine the following Figure 2 The secondary power supply health status assessment scheme for high-temperature aging test equipment provided in this application is described using a specific example.

[0048] Provided in this specific example is a method for evaluating the health status of a secondary power supply of a high-temperature aging test equipment based on a residual convolutional neural network and multi-distance metric fusion. The core is: during the aging test, the secondary power supply of the high-temperature aging test equipment will generate monitoring signals such as voltage, current, power supply ripple fluctuation rate, and MOSFET shell temperature to junction temperature; the directly measured monitoring signal data volume is large and contains a large amount of redundant information. First, feature extraction is performed on the directly measured data to obtain a feature sequence with high information density; the ResNet (Residual Neural Network) model is used to adaptively analyze the feature sequence data to automatically identify the degradation starting point of the secondary power supply of the high-temperature aging test equipment.

[0049] The distance metric algorithm is used to quantitatively calculate the health status of the characteristic sequence in the degradation stage and complete the health assessment of the secondary power supply of the high-temperature aging test equipment.

[0050] The process of the secondary power supply health status assessment method for high temperature aging test equipment based on residual convolutional neural network and multi-distance metric fusion may specifically include the following process: S1: High temperature aging test equipment secondary power signal monitoring: High temperature aging test equipment Figure 2 The high temperature aging bench shown in Figure 2. During the aging test, the voltage and current signals of the secondary power supply of the high temperature aging test equipment are monitored by sensors to obtain large-scale monitoring data for the status evaluation of the switching power supply.

[0051] S2: Preliminary feature extraction.

[0052] Since the amount of directly measured monitoring signal data is large and contains a lot of redundant information, it is first necessary to extract the features of the direct monitoring data to obtain a feature sequence with high information density, namely the multi-dimensional degradation characteristics of the secondary power supply of the high-temperature aging test equipment.

[0053] S3: Input the multi-dimensional degradation characteristics of the secondary power supply of the high-temperature aging test equipment into the residual convolutional neural network model, automatically identify the degradation starting point, and obtain the healthy baseline and degradation characteristics.

[0054] The health assessment process is further realized based on the extracted feature sequence: First, the residual convolutional neural network model is used to identify the degradation starting point of the secondary power supply temperature data and its feature data of the high-temperature aging test equipment, eliminating the instability caused by artificially setting the health baseline to construct health indicators. The ResNet model is based on the residual structure. By constructing "shortcut connections", the input of each layer is directly added to the output after operations such as convolution, ensuring the stability of data feature extraction and learning. The ResNet model uses convolutional layers to extract features. Its convolutional layer acts on the input high-temperature aging test equipment secondary power supply time series data in the form of a sliding window. The convolution kernel focuses on different local time intervals while sliding along the time axis, accurately capturing the feature patterns therein, and excavating the inherent laws hidden in the time series data that can reflect the changes in the equipment's operating status. The ResNet model reasonably stacks multiple modules containing convolutional layers, activation functions, and residual connections, so that the time series data of the secondary power supply of the high-temperature aging test equipment can extract rich and valuable feature information after layer-by-layer processing. After that, the fully connected layer integrates the many features extracted previously and maps the feature vectors into a two-dimensional probability space, thereby accurately identifying the starting point of secondary power degradation of the high-temperature aging test equipment, providing a strong basis for knowing the changes in the health status of the equipment in advance and arranging maintenance plans reasonably.

[0055] Specifically, during the training phase of the ResNet model, training data with health status labels (i.e., training time series degradation feature samples and degradation feature status labels of 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 ResNet 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 ResNet model to automatically segment the healthy baseline of the test data.

[0056] In the test phase of the ResNet model, input test data (i.e., secondary power supply test timing degradation feature samples) , the ResNet 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.

[0057] S4: A multi-distance metric fusion method is used to calculate the distance between the healthy baseline and the degradation characteristics, and the state degradation curve and the current health of the secondary power supply of the high-temperature aging test equipment are determined.

[0058] In order to further realize the health status assessment of the secondary power supply of high-temperature aging test equipment 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, which provides reliable data support for further remaining service life prediction research of the secondary power supply of high-temperature aging test equipment.

[0059] An embodiment of the present application also provides a method for deep fusion of time-frequency characteristics of a secondary power supply of a high-temperature aging test equipment, which is used to extract features from a secondary power supply monitoring signal during a health status assessment of a secondary power supply of a high-temperature aging test equipment.

[0060] As attached Figure 3 As shown, the method for deep fusion of time-frequency characteristics of secondary power supply of high temperature aging test equipment in the embodiment of the present application includes the following steps: Step 301: Collecting a fault data set of components of a high temperature aging test equipment.

[0061] The high-temperature aging test equipment provided in this application can perform non-destructive aging testing on the test piece. The time-frequency domain feature processing method performed in the embodiment of this application can extract features of the status data, health data, etc. of the high-temperature aging test equipment components. The extracted features can be used for health assessment, fault prediction, etc. of the aging test equipment components, thereby ensuring the safety of the test piece. Aging test equipment is high-temperature aging test equipment, which can also be called a high-temperature aging bench or simply an aging bench. The secondary power supply is the secondary power supply described above.

[0062] Among them, the fault data includes multiple key parameter timing data, and the key parameters are different for different devices. For example: when the device is a secondary switching power supply, the key parameters can be the power supply voltage, current signal, and the thermal resistance characteristics from the MOSFET shell temperature to the junction temperature synchronously monitored by the infrared thermal imager and the embedded temperature sensor. When the device is a test chamber, the key parameters can be temperature monitoring signals, etc. These monitoring signals can be used to evaluate the health status of the aging test equipment devices, but in the actual implementation process, the amount of directly measured key parameter data is large and contains a lot of redundant information. Therefore, it is necessary to use the feature extraction model trained by the deep fusion method of the time-frequency characteristics of the secondary power supply of the high-temperature aging test equipment in the embodiment of the present application to perform preliminary feature extraction on the directly measured data to obtain a feature sequence with high information density.

[0063] The fault data set of the old test equipment device collected in the embodiment of the present application can be a fault data set of the entire process from the beginning of degradation to the cessation of operation (i.e., the end of the remaining life cycle) of the device.

[0064] When collecting the fault data set of the aging test equipment, it is necessary to monitor the load change characteristics of the secondary switching power supply, including the output voltage and current transient response under dynamic load switching. For the load fluctuation when the device under test is connected, record the power supply ripple fluctuation rate caused by it. The calculation formula is: Ripple Fluctuation Rate = ( / )×100% in, is the peak-to-peak output voltage, is the average value of the output voltage. The ripple waveform is captured by a high-precision oscilloscope, and its spectrum characteristics are analyzed to identify abnormal harmonic components. Devices may include but are not limited to: aging bench switching power supply, aging bench high temperature test chamber, aging bench high temperature and high humidity test chamber, etc. The aging bench switching power supply can be further subdivided into: primary switching power supply, secondary switching power supply and secondary switching power supply MOSFET switch tube. In addition, aging bench devices may also include: drive control detection board.

[0065] The specific components included in the aging test equipment can be flexibly adjusted according to the type of the aging test equipment. The specific type of the aging table is not limited in the embodiment of the present application. For example, the aging test equipment can be a high-temperature and high-humidity aging table, a constant temperature and constant pressure aging table, etc.

[0066] Step 302: pre-process the fault data set to obtain a training data set and a test data set.

[0067] An optional method of preprocessing the fault data set to obtain a training data set and a test data set may include the following sub-steps: Sub-step 1: For each key parameter in the fault data set, perform sliding window cutting on the key parameter time series data to construct a sample data set; When constructing a sample data set by sliding window cutting, the data in the load mutation period needs to be adaptively segmented. For intervals where the load change rate exceeds the threshold (such as ±10% / ms), a smaller window width and step size are used to capture the detailed characteristics of the transient response. At the same time, dynamic baseline calibration is performed on the current and voltage output data to eliminate the baseline drift caused by the connection of the device under test.

[0068] Sub-step 2: normalize each data sample in the sample data set to obtain a normalized sample data set; Sub-step 3: Select a preset percentage of sample data from the normalized sample data set as the training data set, and use the remaining sample data as the test data set.

[0069] The training data set is used to train the preset model, and the test data set is used to verify the prediction accuracy of the feature extraction model after training. The preset percentage can be flexibly set by those skilled in the art, and is not specifically limited in the embodiments of the present application, for example: set to 60%-80%, preferably 70%.

[0070] Step 303: Perform short-time Fourier transform on the time series data of each key parameter in the training data set to obtain a time-frequency diagram.

[0071] Traditional time domain and frequency domain analysis methods are mostly for stationary signals, and can only obtain information in one aspect of the time domain or frequency domain. In actual devices such as secondary switching power supply applications, the collected signals are mostly non-stationary, and analysis in one aspect can no longer meet the needs, so it is necessary to determine the relationship between the signal frequency and time. The joint time-frequency analysis method is a very effective tool in the current process of processing non-stationary signals. Therefore, short-time Fourier transform is used in this application to process variable time series and non-stationary signals. The basic principle of STFT (i.e. short-time Fourier transform) is: use a finite time window function h(t) to intercept the vibration signal, perform Fourier transform on the obtained signal, obtain a small range of local spectrum in this time period, and through the movement of the window function h(t) on the time axis, gradually analyze the signal band to obtain a set of local "spectra" of the signal. STFT is essentially a transformation of the basis function.

[0072] An optional method of performing short-time Fourier transform on each time series data in the training data set to obtain a time-frequency diagram may include the following sub-steps: Sub-step 1: for each time series data in the training data set, a window function with a finite time length is used to intercept the vibration signal in the time series data; Sub-step 2: Perform Fourier transform on the intercepted vibration signal to obtain a local spectrum in a small range of the corresponding time period; The transformation formula of the signal collected by the aging station device such as the secondary power supply can be:

[0073] Where: is the source signal, is the analysis window function; is the time spectrum at time t.

[0074] Sub-step 3: By moving the window function on the time axis, each vibration signal band is gradually analyzed to obtain a set of local spectra of the vibration signal and generate a time-frequency diagram.

[0075] Preferably, the window function is a Hanning window function, which has good smoothness and is suitable for reducing spectrum leakage. The expression of the Hanning window function is as follows:

[0076] Step 304: Randomly mask each time-frequency graph and input it into a preset model for training to generate a feature extraction model.

[0077] Through steps 301 to 304, time-frequency diagrams corresponding to different time segments of multiple key parameters can be obtained. For each time-frequency diagram, the time-frequency diagram can be masked by random masking. The time-frequency diagram is different from the time series feature and the frequency domain feature. The time series feature and the frequency domain feature are one-dimensional data. The time-frequency diagram is a two-dimensional picture. The horizontal axis represents time and the vertical axis represents frequency. The depth of color represents the difference in frequency amplitude.

[0078] The randomly masked time-frequency graphs are sent to preset models such as the Transformer autoencoder for training, and a feature extraction model is generated after the training is completed.

[0079] Transformer is a model built on the Seq-to-Seq framework. Compared with classic deep learning models, the most prominent advantage of Transformer is the use of a multi-head attention mechanism. The purpose of the multi-head attention layer is to give different importance to words / tokens in a sequence from multiple aspects. The Transformer model is mainly composed of input, encoder, decoder and output.

[0080] Position encoding: The position encoding layer is used to determine the position information of the sequence. Since there is no recursive layer and convolution layer in RNN and CNN, the input order information cannot be obtained by relying solely on the self-attention mechanism. The order information of the sequence needs to be actively passed to the model. The Transformer model uses a combination of sine and cosine functions to positionally encode the sequence. The calculation method is as follows:

[0081]

[0082] Where: is the position of the current sequence; for dimension; is the dimension of the input features.

[0083] Multi-head attention mechanism: The multi-head attention mechanism uses multiple attention mechanisms to perform operations in parallel, and then splices the operation results together through linear transformation. The core technology in Transformer is the multi-head attention mechanism, which uses the multi-head attention mechanism to extract the dependency features between data, capture the correlation between data and establish a context prediction model. The calculation method is as follows:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] Where: is the query matrix; is the key matrix; is the value matrix; , , is a trainable parameter matrix; is the processed input; is the dimension of the key matrix; , , , is the learnable parameter matrix.

[0090] Feedforward network, summation and normalization: In the Transformer model, the encoding part and the decoding part also contain feedforward network, summation and normalization. The calculation formula of the feedforward neural network is as follows:

[0091] Where: For input; , , , are parameters that can be obtained through training.

[0092] The calculation formula for summation and normalization is as follows:

[0093] Where: For input; This is the result after module processing.

[0094] In an optional embodiment, each time-frequency graph is randomly masked and input into a preset model for training, and a method of generating a feature extraction model may include the following sub-steps: Sub-step 1: Input the randomly masked time-frequency graph into the preset model, send it into the encoding module after position encoding, and send the output of the encoding module into the decoder, and restore the feature parameters in the high-dimensional hidden layer into the time-frequency graph before masking through the decoder; Sub-step 2: using the restored time-frequency graph to iteratively train the preset model multiple times to adjust the model parameters; Among them, the model parameters include: embedding dimension, number of attention heads, number of encoder layers, and number of decoder layers.

[0095] Sub-step 3: Take out the encoder layer and decoder layer of the model after multiple iterations of adjustment, and retain the weight parameters of each layer.

[0096] Sub-step 4: Generate a feature extraction model based on each encoder layer, each decoder layer and each layer weight parameter.

[0097] Step 305: Test the feature extraction model based on the test data set.

[0098] After training the preset model based on the training data set (also called pre-training) to generate a feature extraction model, the feature extraction model can be tested based on the test data in the test data set to determine the accuracy of the prediction results of the feature extraction model.

[0099] Based on the pre-trained Transforme autoencoder model, the training data set Perform self-attention feature extraction to obtain the self-attention feature set .

[0100] Step 306: After the test is passed, the key parameter timing data of the device to be predicted is input into the feature extraction model to predict the target time-frequency domain features.

[0101] The method for deep fusion of time-frequency features of secondary power supply of high-temperature aging test equipment provided in the embodiment of the present application collects the fault data set of aging test equipment components; pre-processes the fault data set to obtain a training data set and a test data set; performs short-time Fourier transform on the time series data of each key parameter in the training data set to obtain a time-frequency diagram; randomly masks each time-frequency diagram and inputs it into a preset model for training to generate a feature extraction model; tests the feature extraction model according to the test data set; after the test is passed, the key parameter time series data of the device to be predicted is input into the feature extraction model to predict the target time-frequency domain features. The method provided in the embodiment of the present invention fully coordinates and refines a large amount of fault data of aging test equipment components, and randomly masks the time-frequency diagram obtained by short-time Fourier transform of the segmented time series data, and uses the time-frequency diagram after random masking to train the preset model, which can fully refine the local semantic information of the aging station components, overcome the constraints of data scarcity, shallow annotation, etc. on the training model, and the trained feature extraction model predicts the time-frequency features more accurately and reliably. Furthermore, the health status of the aging test equipment components obtained based on the predicted time-frequency feature analysis is more reliable.

[0102] Figure 4 A structural block diagram of a secondary power supply health status assessment device for high-temperature aging test equipment to implement an embodiment of the present application.

[0103] The high temperature aging test equipment 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 the secondary power supply monitoring signal of the high temperature aging test equipment to be evaluated, wherein the monitoring signal includes: current signal, voltage output signal, power supply ripple fluctuation rate, MOSFET device case temperature to junction temperature, etc.; An extraction module 402 is used to extract features from the current signal and the voltage output signal, and to obtain a multi-dimensional feature sequence by mixing the power supply ripple fluctuation rate, the MOSFET shell temperature to junction temperature and other degradation curves; The prediction module 403 is used to input the multi-dimensional feature sequence into a pre-trained residual convolutional neural network model to obtain the degradation characteristic state of the secondary power supply of the high temperature aging test equipment, wherein the degradation characteristic state includes: a healthy state and a degraded state; The determination module 404 is used to determine the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated based on the probability space points after the multi-dimensional feature sequence mapping and the health baseline in the residual convolutional neural network model when the secondary power supply of the high-temperature aging test equipment is in a degraded state, wherein the state degradation curve can characterize the health state of the secondary power supply of the high-temperature aging test equipment.

[0104] Optionally, the residual convolutional neural network model includes: a convolution layer, a batch normalization layer, an activation function, a maximum pooling layer, multiple residual blocks, a global average pooling layer and a fully connected layer.

[0105] Optionally, the device further includes a training module for training a residual convolutional neural network model, wherein the training module includes: The first submodule is used to obtain the training timing degradation feature samples of the secondary power supply of the high temperature aging test equipment; wherein each training timing degradation feature sample is marked with a degradation feature state; The second submodule is used to input the training time series degradation feature sample into a pre-established residual convolutional neural network model to obtain a mapped first probability space point; The third submodule is used to determine the starting point of the secondary power supply degradation of the high-temperature aging test equipment according to the two-dimensional first probability space point, and generate a secondary power supply health baseline of the high-temperature aging test equipment.

[0106] Optionally, the training module further includes: The fourth submodule is used to obtain the timing degradation characteristic samples of the secondary power supply test of the high temperature aging test equipment; A fifth submodule is used to input the test time series degradation feature sample into the residual convolutional neural 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.

[0107] 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 health of the second power supply of the high temperature aging test equipment.

[0108] The embodiments of the present application provide Figure 4 The high temperature aging test equipment secondary power supply health status assessment device shown can achieve Figure 1 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0109] The high-temperature aging test equipment secondary power supply health status assessment device provided in the embodiment of the present application collects the secondary power supply monitoring signal of the high-temperature aging test equipment to be evaluated; generates a multidimensional feature sequence based on the monitoring signal; inputs the multidimensional feature sequence into a pre-trained residual convolutional neural network model to obtain the degradation feature state of the secondary power supply of the high-temperature aging test equipment; when the secondary power supply of the high-temperature aging test equipment is in a degraded state, based on the probability space points mapped by the multidimensional feature sequence and the health baseline in the residual convolutional neural network model, the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated is determined, thereby accurately determining the health state of the secondary power supply of the high-temperature aging test equipment. The high-temperature aging test equipment secondary power supply health status assessment device provided in the embodiment of the present application collects the secondary power supply monitoring signal and extracts the multidimensional feature sequence, and predicts the multidimensional feature sequence based on the trained model to obtain the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated, thereby accurately determining the health state of the secondary power supply of the high-temperature aging test equipment.

[0110] 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.

[0111] Memory, used to store computer programs; The processor is used to implement the secondary power supply health status assessment method of high temperature aging test equipment shown in the above method embodiment when executing the program stored in the memory.

[0112] 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.

[0113] The communication interface is used for communication between the above terminal and other devices.

[0114] 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.

[0115] 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 run on an electronic device, the electronic device implements the secondary power supply health status assessment method of high-temperature aging test equipment described in any of the above embodiments.

[0116] 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 secondary power supply health status assessment method of high-temperature aging test equipment described in any of the above embodiments.

[0117] 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.

[0118] 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 secondary power supply of high temperature aging test equipment, characterized in that: The method comprises: Collecting secondary power supply monitoring signals of the high-temperature aging test equipment to be evaluated; wherein the monitoring signals include: current signal, voltage output signal, power supply ripple fluctuation rate, MOSFET case temperature to junction temperature; After extracting features from the current signal and the voltage output signal, the power supply ripple fluctuation rate and the MOSFET shell temperature to junction temperature degradation curve are mixed to obtain a multi-dimensional feature sequence; Inputting the multidimensional feature sequence into a pre-trained residual convolutional neural network model to obtain the degradation feature state of the secondary power supply of the high temperature aging test equipment, wherein the degradation feature state includes: a healthy state and a degraded state; When the secondary power supply of the high-temperature aging test equipment is in a degraded state, the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated is determined based on the probability space points after mapping the multi-dimensional feature sequence and the health baseline in the residual convolutional neural network model, wherein the state degradation curve can characterize the health state of the secondary power supply of the high-temperature aging test equipment.

2. The method according to claim 1, characterized in that The residual convolutional neural network model includes: a convolution layer, a batch normalization layer, an activation function, a maximum pooling layer, multiple residual blocks, a global average pooling layer and a fully connected layer.

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

4. The method according to claim 3, characterized in that The method further comprises: Obtain the timing degradation characteristic samples of the secondary power supply test of the high temperature aging test equipment; Inputting the test time series degradation feature sample into the residual convolutional neural 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 health of the second power supply of the high temperature aging test equipment.

6. A secondary power supply health status assessment device for high temperature aging test equipment, characterized in that: The device comprises: The acquisition module is used to collect the secondary power supply monitoring signal of the high-temperature aging test equipment to be evaluated; wherein the monitoring signal includes: current signal, voltage output signal, power supply ripple fluctuation rate, MOSFET shell temperature to junction temperature; An extraction module, used for extracting features from the current signal and the voltage output signal, and then mixing the power supply ripple fluctuation rate and the MOSFET shell temperature to junction temperature degradation curve to obtain a multi-dimensional feature sequence; A prediction module, used for inputting the multi-dimensional feature sequence into a pre-trained residual convolutional neural network model to obtain a degradation characteristic state of the secondary power supply of the high temperature aging test equipment, wherein the degradation characteristic state includes: a healthy state and a degraded state; A determination module is used to determine, when the secondary power supply of the high-temperature aging test equipment is in a degraded state, the state degradation curve of the secondary power supply of the high-temperature aging test equipment to be evaluated based on the probability space points after mapping the multi-dimensional feature sequence and the health baseline in the residual convolutional neural network model, wherein the state degradation curve can characterize the health state of the secondary power supply of the high-temperature aging test equipment.

7. The device according to claim 6, characterized in that The residual convolutional neural network model includes: a convolution layer, a batch normalization layer, an activation function, a maximum pooling layer, multiple residual blocks, a global average pooling layer and a fully connected layer.

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

9. The device according to claim 8, characterized in that The training module also includes: The fourth submodule is used to obtain the timing degradation characteristic samples of the secondary power supply test of the high temperature aging test equipment; A fifth submodule is used to input the test time series degradation feature sample into the residual convolutional neural 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.

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 secondary power supply health status assessment method of high temperature aging test equipment as described in any one of claims 1 to 5 when executing the program stored in the memory.

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