Method and device for detecting and diagnosing secondary power supply fault of high-temperature aging test equipment

Abstract

The embodiment of the invention provides a high-temperature aging test equipment secondary power supply fault detection and diagnosis method and device, and the method comprises the steps: carrying out the fault detection of a secondary switching power supply simulation model, and obtaining the states of a plurality of virtual measurement points, preliminarily constructing an original D matrix according to a plurality of fault types of the secondary switching power supply; monitoring parameters corresponding to the data-based fault detection model are used as virtual measuring points, the D matrix is expanded, the constructed expanded D matrix is used for matching actual test vectors, and fault diagnosis of the secondary switching power supply is achieved.

Description

Technical Field

[0001] The embodiments of the present specification relate to the technical field of non-destructive reliability screening of integrated circuits, and more particularly to a method and device for detecting and diagnosing secondary power supply faults in high-temperature aging test equipment. Background Art

[0002] The aging station is an aging test equipment for electronic components and products. It accelerates various physical and chemical reaction processes inside the components by continuously applying certain electrical stress to the components for a long time, prompting various potential defects inside the components to be exposed as early as possible, thereby evaluating the reliability and durability of the product. The secondary switching power supply of the aging station has a secondary protection function. Its working principle is similar to that of an ordinary switching power supply, but it pays more attention to the protection and monitoring of the circuit. It will continuously monitor the voltage, current and other parameters of the circuit. Once an abnormality is found, it will immediately cut off the power supply or adjust the circuit parameters. 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. When a product fails, it can respond in time and realize post-maintenance based on fault data. However, due to the lack of active protection technology for high-temperature aging products, the existing 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 the damage of millions of test devices, or the adverse effects such as the introduction of additional stress during the test due to product performance degradation, resulting in the aging test being identified as a failure test, resulting in an 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, significantly reduce the failure rate of integrated circuits, and avoid large-scale electronic system failures and shutdowns such as new energy vehicles, civil airliners, energy storage and transmission stations due to integrated circuit failures.

[0003] During the component aging test process, once the secondary switching power supply fails, such as short circuit or overload, it will have a great impact on the aging station equipment, test process, test results, test environment, etc. Therefore, how to discover the potential hidden dangers inside the power supply in advance, repair or replace it in time, ensure the safety of equipment personnel, and successfully complete the device aging test is a key technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, the embodiments of this specification provide a method for detecting and diagnosing secondary power supply faults of high temperature aging test equipment. One or more embodiments of this specification also relate to a device for detecting and diagnosing secondary power supply faults of high temperature aging test equipment, a computing device, a computer-readable storage medium and a computer program to solve the technical defects existing in the prior art.

[0005] According to a first aspect of an embodiment of this specification, a secondary power supply fault detection and diagnosis method for high temperature aging test equipment is provided, comprising: By real-time monitoring of the secondary power supply of the target high-temperature aging test equipment, real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment is obtained; By setting test thresholds and 0 / 1 logic conversion rules for different fault types, the real-time monitoring data is constructed into a target one-dimensional test vector; Based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix, the fault type of the secondary power supply of the target high-temperature aging test equipment is determined and output.

[0006] Preferably, it also includes: According to the types of electronic components in the secondary power supply of the high-temperature aging test equipment and the connection relationship between the electronic components, a simulation model of the secondary power supply of the high-temperature aging test equipment is constructed, and the simulation model of the secondary power supply of the high-temperature aging test equipment is used to obtain fault monitoring data of the secondary power supply of the high-temperature aging test equipment under different fault types; A data-driven fault diagnosis model is established, and the data-driven fault diagnosis model is trained using the fault monitoring data of the secondary power supply of the high-temperature aging test equipment under different fault types to obtain a trained data-driven fault diagnosis model.

[0007] Preferably, it also includes: By injecting real fault data into the simulation model of the secondary power supply of the high temperature aging test equipment, fault monitoring data of the secondary power supply of the high temperature aging test equipment under different fault types are obtained; According to the set test threshold and the first 0 / 1 logic conversion rule, the fault monitoring data under the different fault types are respectively converted into the first 0 / 1 logic value; Based on the first 0 / 1 logic value, a physical one-dimensional test vector under different fault types is obtained, and an original D matrix is ​​constructed according to the physical one-dimensional test vector under the different fault types.

[0008] Preferably, it also includes: The fault diagnosis results under different fault types are obtained by respectively inputting the fault monitoring data of the secondary power supply of the high temperature aging test equipment under different fault types into the trained data-driven fault diagnosis model; According to the set second 0 / 1 logic conversion rule, the fault diagnosis results under the different fault types are respectively converted into second 0 / 1 logic values; Based on the second 0 / 1 logical value, virtual one-dimensional test vectors under different fault types are obtained, and the virtual one-dimensional test vectors under different fault types are added to the original D matrix to obtain an extended D matrix; wherein the extended D matrix contains different fault types and their corresponding one-dimensional test vectors, and the one-dimensional test vectors are composed of physical one-dimensional test vectors and virtual one-dimensional test vectors.

[0009] Preferably, the 0 / 1 logic conversion rule includes the first 0 / 1 logic conversion rule and the second 0 / 1 logic conversion rule, wherein the step of constructing the real-time monitoring data into a target one-dimensional test vector by setting test thresholds and 0 / 1 logic conversion rules for different fault types includes: By comparing the real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment with the test thresholds set for different fault types, a comparison result is obtained, and based on the comparison result and the first 0 / 1 logic conversion rule, a physical one-dimensional test vector is obtained; By inputting the real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment into the data-driven fault diagnosis model, a fault diagnosis result of the secondary power supply of the target high-temperature aging test equipment is obtained, and based on the fault diagnosis result and the second 0 / 1 logic conversion rule, a virtual one-dimensional test vector is obtained; Based on the physical one-dimensional test vector and the virtual one-dimensional test vector, a target one-dimensional test vector of the secondary power supply of the target high-temperature aging test equipment is obtained.

[0010] Preferably, based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix, the fault type of the secondary power supply of the target high-temperature aging test equipment is determined and outputted, including: Calculating the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix; The maximum value of the similarities between the target one-dimensional test vector and the one-dimensional test vectors corresponding to the multiple fault types is determined, and the fault type corresponding to the maximum value is determined as the fault type of the secondary power supply of the target high-temperature aging test equipment and outputted.

[0011] Preferably, calculating the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix includes: Respectively calculating the Mahalanobis distance, cosine similarity and Manhattan distance between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix; By performing multi-distance metric fusion on the Mahalanobis distance, the cosine similarity and the Manhattan distance, the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix is ​​obtained, which includes:

[0012] in, represents the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix, represents the Mahalanobis distance; represents cosine similarity; represents Manhattan distance; , and denote weights, and + + =1.

[0013] According to a second aspect of the embodiments of this specification, a secondary power supply fault detection and diagnosis device for high temperature aging test equipment is provided, comprising: An acquisition module is configured to obtain real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment by real-time monitoring of the secondary power supply of the target high-temperature aging test equipment; A construction module is configured to construct the real-time monitoring data into a target one-dimensional test vector by setting test thresholds and 0 / 1 logic conversion rules for different fault types; The fault detection and diagnosis module is configured to determine and output the fault type of the secondary power supply of the target high-temperature aging test equipment based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix.

[0014] According to a third aspect of an embodiment of this specification, a computing device is provided, including: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement any step of the secondary power supply fault detection and diagnosis method of high temperature aging test equipment.

[0015] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of any one of the methods for detecting and diagnosing secondary power supply faults in high-temperature aging test equipment are implemented.

[0016] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned high-temperature aging test equipment secondary power supply fault detection and diagnosis method.

[0017] In the aging test process of the embodiment of this specification, the secondary switching power supply of the aging test bench will generate monitoring signals such as voltage and current. First, by performing fault detection on the secondary switching power supply simulation model, the status of multiple virtual measurement points is obtained, and the original D matrix is ​​preliminarily constructed according to the multiple fault types of the secondary switching power supply; secondly, since the D matrix may have undetectable faults, fuzzy groups, or fault types that are not covered, the monitoring parameters corresponding to the data-based fault detection model can be used as virtual measurement points to expand the D matrix and solve the problems and deficiencies in the above-mentioned original D matrix. Finally, the constructed extended D matrix is ​​used to match the actual test vector to achieve fault diagnosis of the secondary switching power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a secondary power supply fault detection and diagnosis method for high temperature aging test equipment provided by one embodiment of this specification; Figure 2 It is a schematic diagram of another secondary power supply fault detection and diagnosis process of high temperature aging test equipment provided by an embodiment of this specification; Figure 3 is a schematic diagram of a process from an original D matrix to an expanded D matrix provided by an embodiment of the present specification; Figure 4 It is a structural schematic diagram of a secondary power supply fault detection and diagnosis device for high temperature aging test equipment provided by one embodiment of this specification; Figure 5 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0019] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0020] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0022] In this specification, a secondary power supply fault detection and diagnosis method for high-temperature aging test equipment is provided. This specification also involves a secondary power supply fault detection and diagnosis device for high-temperature aging test equipment, a computing device, a computer-readable storage medium, and a computer program, which are described in detail one by one in the following embodiments.

[0023] Figure 1 A flow chart of a secondary power supply fault detection and diagnosis method for high temperature aging test equipment provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0024] Step S101: obtaining real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment by real-time monitoring of the secondary power supply of the target high-temperature aging test equipment; Step S102: constructing the real-time monitoring data into a target one-dimensional test vector by setting test thresholds and 0 / 1 logic conversion rules for different fault types; Step S103: Based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix, the fault type of the secondary power supply of the target high-temperature aging test equipment is determined and output.

[0025] In an optional implementation, it also includes: According to the types of electronic components in the secondary power supply of the high-temperature aging test equipment and the connection relationship between the electronic components, a simulation model of the secondary power supply of the high-temperature aging test equipment is constructed, and the simulation model of the secondary power supply of the high-temperature aging test equipment is used to obtain fault monitoring data of the secondary power supply of the high-temperature aging test equipment under different fault types; A data-driven fault diagnosis model is established, and the data-driven fault diagnosis model is trained using the fault monitoring data of the secondary power supply of the high-temperature aging test equipment under different fault types to obtain a trained data-driven fault diagnosis model.

[0026] In an optional implementation, it also includes: By injecting real fault data into the simulation model of the secondary power supply of the high temperature aging test equipment, fault monitoring data of the secondary power supply of the high temperature aging test equipment under different fault types are obtained; According to the set test threshold and the first 0 / 1 logic conversion rule, the fault monitoring data under the different fault types are respectively converted into the first 0 / 1 logic value; Based on the first 0 / 1 logic value, a physical one-dimensional test vector under different fault types is obtained, and an original D matrix is ​​constructed according to the physical one-dimensional test vector under the different fault types.

[0027] In an optional implementation, it also includes: The fault diagnosis results under different fault types are obtained by respectively inputting the fault monitoring data of the secondary power supply of the high temperature aging test equipment under different fault types into the trained data-driven fault diagnosis model; According to the set second 0 / 1 logic conversion rule, the fault diagnosis results under the different fault types are respectively converted into second 0 / 1 logic values; Based on the second 0 / 1 logical value, virtual one-dimensional test vectors under different fault types are obtained, and the virtual one-dimensional test vectors under different fault types are added to the original D matrix to obtain an extended D matrix; wherein the extended D matrix contains different fault types and their corresponding one-dimensional test vectors, and the one-dimensional test vectors are composed of physical one-dimensional test vectors and virtual one-dimensional test vectors.

[0028] In an optional implementation, the 0 / 1 logic conversion rule includes the first 0 / 1 logic conversion rule and the second 0 / 1 logic conversion rule, wherein the step of constructing the real-time monitoring data into a target one-dimensional test vector by setting test thresholds and 0 / 1 logic conversion rules for different fault types includes: By comparing the real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment with the test thresholds set for different fault types, a comparison result is obtained, and based on the comparison result and the first 0 / 1 logic conversion rule, a physical one-dimensional test vector is obtained; By inputting the real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment into the data-driven fault diagnosis model, a fault diagnosis result of the secondary power supply of the target high-temperature aging test equipment is obtained, and based on the fault diagnosis result and the second 0 / 1 logic conversion rule, a virtual one-dimensional test vector is obtained; Based on the physical one-dimensional test vector and the virtual one-dimensional test vector, a target one-dimensional test vector of the secondary power supply of the target high-temperature aging test equipment is obtained.

[0029] In an optional implementation, based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix, the fault type of the secondary power supply of the target high temperature aging test equipment is determined and outputted, including: Calculating the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix; The maximum value of the similarities between the target one-dimensional test vector and the one-dimensional test vectors corresponding to the multiple fault types is determined, and the fault type corresponding to the maximum value is determined as the fault type of the secondary power supply of the target high-temperature aging test equipment and outputted.

[0030] In an optional implementation, calculating the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix includes: Respectively calculating the Mahalanobis distance, cosine similarity and Manhattan distance between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix; By performing multi-distance metric fusion on the Mahalanobis distance, the cosine similarity and the Manhattan distance, the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix is ​​obtained, which includes:

[0031] in, represents the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix, represents the Mahalanobis distance; represents cosine similarity; represents Manhattan distance; , and denote weights, and + + =1.

[0032] In summary, during the aging test process of the embodiments of this specification, the secondary switching power supply of the aging test bench will generate monitoring signals such as voltage and current. First, by performing fault detection on the secondary switching power supply simulation model, the states of multiple virtual measuring points are obtained, and the original D matrix is ​​preliminarily constructed according to the multiple fault types of the secondary switching power supply; secondly, since the D matrix may have undetectable faults, fuzzy groups, or fault types that are not covered, the monitoring parameters corresponding to the data-based fault detection model can be used as virtual measuring points to expand the D matrix and solve the problems and deficiencies in the above-mentioned original D matrix. Finally, the constructed extended D matrix is ​​used to match the actual test vector to achieve fault diagnosis of the secondary switching power supply.

[0033] Figure 2 FIG. 1 is a schematic diagram of another secondary power supply fault detection and diagnosis process of a high temperature aging test device provided by an embodiment of the present specification, such as Figure 2 As shown, including: Step S1: Aging test bench (also known as high temperature aging test equipment) secondary switch power supply fault simulation model (obtaining fault data); According to the types of electronic components in the secondary switching power supply and the connection relationship between the electronic components, a simulation model of the secondary switching power supply is constructed; the current and voltage data of the secondary switching power supply in the aging station under all states (including fault state and normal state) are obtained by fault simulation injection or physical fault injection.

[0034] Step S2: using monitoring data based on a data-driven fault detection model as a virtual measurement point; According to the current and voltage data of the secondary switch power supply fault obtained in step S1, a data-driven fault diagnosis model is established to perform fault detection on the secondary switch power supply, and the state of the virtual measurement point is obtained, wherein the virtual measurement point is the detection result of the secondary switch power supply based on data drive, including the fault mode and the normal state. After the state of the virtual measurement point is obtained, it is expanded into the D matrix to obtain the extended D matrix. The advantage of using the monitoring parameter fault characteristics corresponding to the data-driven fault diagnosis model as the virtual measurement point is that the diagnosis model is adaptive, robust and contains more abundant fault information, wherein the diagnosis model is: secondary switch power supply fault detection model. Among them, the main function of the secondary switch power supply fault detection model is to extract the corresponding fault characteristics from the detected current and voltage signals and complete the fault diagnosis, and monitor whether there is a fault and the fault mode that occurs.

[0035] The monitoring parameter fault characteristics at least include: current, voltage 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. Among them, the essential characteristics of ripple voltage change: the root cause of high-frequency ripple sudden increase is: output capacitor ESR increase (electrolytic capacitor drying up), synchronous rectifier MOSFET switch timing abnormality, input capacitor failure; low-frequency ripple distortion fault root cause: feedback loop compensation failure (phase margin <30°), PWM controller power supply voltage fluctuation. The essential characteristics of average output voltage deviation: voltage increase: PWM duty cycle locked at the maximum value, optocoupler isolation failure (CTR value drops> 50%); voltage reduction: output impedance increases significantly, power circuit parasitic resistance increases (PCB copper foil temperature rise> 40℃); voltage oscillation; loop gain exceeds the critical value, compensation network capacitor capacitance drift (such as Ccomp deviation> 20%). The essential characteristics of inductor current abnormality: peak-to-peak increase: inductor saturation (L value drops> 30%) or input voltage overshoot; peak-to-peak decrease: switching frequency increases abnormally. The essential characteristics of abnormal capacitor current: peak-to-peak value change law: spike burrs: increased ESL leads to resonance, flat-top distortion: capacitor value attenuation.

[0036] The impact of DUT (device under test) on the secondary power supply is a complex and multidimensional issue, which can be mainly attributed to the four dimensions of load characteristics, parasitic coupling, thermal interaction and feedback interference. Load characteristics are one of the key factors that directly affect the DUT on the secondary power supply. As the load of the secondary power supply, the impedance, power demand and dynamic load change characteristics of the DUT will directly affect the output voltage, current and stability of the secondary power supply. Parasitic coupling is also an influencing factor that cannot be ignored. In complex electronic systems, the DUT and the secondary power supply may produce coupling effects through parasitic components such as capacitors and inductors, resulting in power supply noise, ripple and other problems. Thermal interaction is another important dimension. During the operation of electronic equipment, both the DUT and the secondary power supply will generate a certain amount of heat. If the thermal design is not appropriate, this heat may accumulate inside the device, causing the temperature to rise. A high temperature environment will not only affect the performance and life of the secondary power supply, but may also have an adverse effect on the reliability and stability of the DUT. Feedback interference is also an important aspect of the DUT's impact on the secondary power supply. This feedback interference may cause problems such as instability of the output voltage of the secondary power supply, increased ripple, and in severe cases may even damage the power supply or cause the system to crash.

[0037] Step S3: constructing an original D matrix according to the monitoring parameters of the physical object; The current and voltage data of the secondary switch power supply of the aging station in all states (including fault state and normal state) are obtained by physical fault injection. According to the detection threshold of the current and voltage data and the first 0 / 1 logic conversion rule, the measured values ​​in different states are converted into 0 / 1 logic values ​​(set to 1 if exceeding the monitoring threshold, and set to 0 if not exceeding the monitoring threshold), and all states are traversed to construct the original D matrix represented by 0 / 1 logic.

[0038] Furthermore, if the monitoring data of current and voltage exceeds the test threshold set for different fault types, the set data of the monitoring data of current and voltage is set to 1 according to the first 0 / 1 logic conversion rule; and if the real-time monitoring data does not exceed the test threshold set for different fault types, the set data of the monitoring data of current and voltage is set to 0 according to the first 0 / 1 logic conversion rule.

[0039] Among them, the monitoring parameters of current and voltage include: ripple voltage, average output voltage, inductor current peak-to-peak value, inductor current peak value, capacitor current peak-to-peak value, and capacitor current peak value. Depending on whether the monitored parameter value exceeds the monitoring threshold, if it exceeds the threshold, it is set to 1, indicating a fault has occurred; if it does not exceed the threshold, it is set to 0, indicating a normal state.

[0040] Step S4: Add the virtual measurement points to the original D matrix to obtain an extended D matrix; The original D matrix under the physical fault injection mode can be obtained by step S3. Since the original D matrix may have undetectable faults, fuzzy groups or uncovered fault modes, the monitoring parameter fault characteristics represented by the data-driven fault detection model are used as virtual measurement points to supplement the original D matrix, expand the D matrix, and construct a complete extended D matrix model to solve the above problems. The monitoring parameters can be parameters such as the input, output and state variables of the system.

[0041] In order to solve the problem of undetectable faults, fuzzy groups, or uncovered fault types in the original D matrix fault diagnosis, this specification proposes a data-driven detection model as a virtual measurement point to expand the D matrix. The advantages are: the diagnosis model is adaptive, robust and contains richer fault information.

[0042] Specifically, the secondary switch power supply fault detection model extracts characteristic information related to the fault from the data and completes the fault diagnosis result as the newly added virtual measuring point after expansion. According to the correlation between the monitoring parameters represented by the fault detection model and different fault modes, specifically, according to whether the fault detection model can detect the fault mode, and according to the second 0 / 1 logic conversion rule (if the fault mode can be detected, it is set to 1, and if it cannot be detected, it is set to 0), the original D matrix is ​​expanded. The specific expansion example of the D matrix is ​​shown in the figure below. The expanded D matrix mainly includes physical measuring points: measuring point A, measuring point B, measuring point C..., and virtual measuring points: secondary switch power supply fault detection model, wherein the secondary switch power supply fault detection model completes the fault diagnosis result of one or more characteristic information related to the fault extracted by the monitoring parameters as the virtual measuring point. According to the correlation between the monitoring parameters represented by these fault detection models and different fault modes, the original D matrix can be expanded, such as Figure 3 shown.

[0043] Furthermore, by inputting the monitoring parameters of the current and voltage of the secondary power supply of the high-temperature aging test equipment into the data-driven fault diagnosis model, a fault diagnosis result is obtained. If the fault diagnosis result is that the fault type is monitored, the set data of the data-driven fault diagnosis model is set to 1 according to the second 0 / 1 logic conversion rule; and if the fault diagnosis result is that the fault type is not monitored, the set data of the data-driven fault diagnosis model is set to 0 according to the second 0 / 1 logic conversion rule.

[0044] Step S5: Construct the entity monitoring vector and obtain the final diagnostic reasoning result.

[0045] Based on the extended D matrix model, the model is analyzed to achieve model simplification and test point optimization, and finally the diagnostic strategy is obtained. First, real-time monitoring data is collected for multiple test points (physical test points and virtual test points) of the secondary switching power supply to obtain the real-time test results of each test point. A one-dimensional test vector is constructed (the test results are combined into a vector) by setting the test threshold and the 0 / 1 logic conversion rule. Then the test vector is compared with each row of the D matrix to find the row closest to the test vector. This is usually achieved by calculating the distance between the test vector and each row of the D matrix (Manhattan distance, cosine similarity, etc.). Finally, the fault state corresponding to the row closest to the test vector is the diagnosed fault state.

[0046] An embodiment of the present application also provides a time-frequency domain feature processing method based on an alternating mask reconstruction mechanism, which is used to obtain real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment during the secondary power supply fault detection and diagnosis process of the high-temperature aging test equipment, wherein the real-time monitoring data includes target time-frequency domain feature data.

[0047] Furthermore, the time-frequency domain feature processing method based on the alternating mask reconstruction mechanism of the embodiment of the present application includes the following steps: Step 201: Collecting a fault data set of the components of the aging station.

[0048] The aging station provided in the present application can perform non-destructive aging testing on the test parts. The time-frequency domain feature processing method used in the embodiments of the present application can extract features of the status data, health data, etc. of the aging station components. The extracted features can be used for health assessment, fault prediction and diagnosis, etc. of the aging station components, thereby ensuring the safety of the test parts.

[0049] 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 station devices, but in the actual implementation process, the amount of data of these key parameters directly measured is large and contains a lot of redundant information. Therefore, it is necessary to use the feature extraction model trained by the time-frequency domain feature processing method based on the alternating mask reconstruction mechanism in the embodiment of the present application to perform preliminary feature extraction on the direct measurement data to obtain a feature sequence with high information density.

[0050] The fault data set of the aging station 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.

[0051] When collecting the fault data set of the aging station device, it is necessary to simultaneously 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.

[0052] The specific components included in the aging station can be flexibly adjusted according to the type of the aging station. The specific type of the aging station is not limited in the embodiments of the present application. For example, the aging station can be a high-temperature and high-humidity aging station, a constant temperature and constant pressure aging station, or a high-temperature aging test equipment.

[0053] Step 202: pre-process the fault data set to obtain a training data set and a test data set.

[0054] 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 2021: 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.

[0055] Sub-step 2022: performing normalization processing on each data sample in the sample data set to obtain a normalized sample data set; Sub-step 2023: Select a preset percentage of sample data from the normalized sample data set as a training data set, and use the remaining sample data as a test data set.

[0056] 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%.

[0057] Step 203: 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.

[0058] 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.

[0059] 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 2031: for each time series data in the training data set, using a window function with a finite time length to intercept the vibration signal in the time series data; Sub-step 2032: performing Fourier transform on the intercepted vibration signal to obtain a local spectrum of a small range in a corresponding time period; The transformation formula of the signal collected by the aging station device such as the secondary power supply can be:

[0060] Where: is the source signal, is the analysis window function; is the time spectrum at time t.

[0061] Sub-step 2033: by moving the window function on the time axis, each vibration signal band is gradually analyzed to obtain a set of local frequency spectra of the vibration signal, and a time-frequency diagram is generated.

[0062] 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:

[0063] Step 204: Randomly mask each time-frequency graph and input it into a preset model for training to generate a feature extraction model.

[0064] Through steps 201 to 204, 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.

[0065] 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.

[0066] 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.

[0067] 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:

[0068]

[0069] Where: is the position of the current sequence; for dimension; is the dimension of the input features.

[0070] 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:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] 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.

[0077] 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:

[0078] Where: For input; , , , are parameters that can be obtained through training.

[0079] The calculation formula for summation and normalization is as follows:

[0080] Where: For input; This is the result after module processing.

[0081] 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 2041: inputting the randomly masked time-frequency graph into a preset model, and sending it into an encoding module after position encoding, and sending the output of the encoding module into a decoder, and restoring the feature parameters in the high-dimensional hidden layer into the time-frequency graph before masking through the decoder; Sub-step 2042: 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.

[0082] Sub-step 2043: Take out the encoder layer and decoder layer of the model after multiple iterations of adjustment, and retain the weight parameters of each layer.

[0083] Sub-step 2044: Generate a feature extraction model based on each encoder layer, each decoder layer and each layer weight parameter.

[0084] Step 205: Test the feature extraction model based on the test data set.

[0085] 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.

[0086] Based on the pre-trained Transforme autoencoder model, the training data set Perform self-attention feature extraction to obtain the self-attention feature set .

[0087] Step 206: 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.

[0088] The time-frequency domain feature processing method based on the alternating mask reconstruction mechanism provided in the embodiment of the present application collects the fault data set of the aging station device; pre-processes the fault data set to obtain the training data set and the 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 passes, 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 the aging station device, and randomly masks the time-frequency diagram obtained by performing short-time Fourier transform on 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 device, 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 diagnosis result of the aging station device obtained based on the predicted time-frequency feature analysis is more reliable.

[0089] Corresponding to the above-mentioned high-temperature aging test equipment secondary power supply fault detection and diagnosis method embodiment, this specification also provides a high-temperature aging test equipment secondary power supply fault detection and diagnosis device embodiment, Figure 4 FIG. 1 is a schematic diagram showing a structure of a secondary power supply fault detection and diagnosis device for high temperature aging test equipment provided by an embodiment of the present specification. Figure 4 As shown, the device comprises: The acquisition module 401 is configured to obtain real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment by real-time monitoring of the secondary power supply of the target high-temperature aging test equipment; A construction module 402 is configured to construct the real-time monitoring data into a target one-dimensional test vector by setting test thresholds and 0 / 1 logic conversion rules for different fault types; The fault detection and diagnosis module 403 is configured to determine and output the fault type of the secondary power supply of the target high temperature aging test equipment based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix.

[0090] The above is a schematic scheme of a secondary power supply fault detection and diagnosis device for high temperature aging test equipment of this embodiment. It should be noted that the technical scheme of the secondary power supply fault detection and diagnosis device for high temperature aging test equipment and the technical scheme of the secondary power supply fault detection and diagnosis method for high temperature aging test equipment belong to the same concept. For details not described in detail in the technical scheme of the secondary power supply fault detection and diagnosis device for high temperature aging test equipment, please refer to the description of the technical scheme of the secondary power supply fault detection and diagnosis method for high temperature aging test equipment.

[0091] Figure 5 The structure block diagram of a computing device 500 provided according to an embodiment of the present specification is shown. The components of the computing device 500 include but are not limited to a memory 510 and a processor 520. The processor 520 is connected to the memory 510 via a bus 530, and the database 550 is used to store data.

[0092] The computing device 500 also includes an access device 540 that enables the computing device 500 to communicate via one or more networks 560. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 540 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a World Wide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0093] In one embodiment of the present specification, the above components of the computing device 500 and Figure 5 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 5The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0094] Computing device 500 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or PC. Computing device 500 may also be a mobile or stationary server.

[0095] The processor 520 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned high-temperature aging test equipment secondary power supply fault detection and diagnosis method.

[0096] The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the secondary power supply fault detection and diagnosis method of the high-temperature aging test equipment mentioned above belong to the same concept, and the details not described in detail in the technical scheme of the computing device can be referred to the description of the technical scheme of the secondary power supply fault detection and diagnosis method of the high-temperature aging test equipment mentioned above.

[0097] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned high-temperature aging test equipment secondary power supply fault detection and diagnosis method.

[0098] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the secondary power supply fault detection and diagnosis method of the high-temperature aging test equipment mentioned above belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the secondary power supply fault detection and diagnosis method of the high-temperature aging test equipment mentioned above.

[0099] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned high-temperature aging test equipment secondary power supply fault detection and diagnosis method.

[0100] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the secondary power supply fault detection and diagnosis method of the high-temperature aging test equipment mentioned above belong to the same concept, and the details not described in detail in the technical scheme of the computer program can be found in the description of the technical scheme of the secondary power supply fault detection and diagnosis method of the high-temperature aging test equipment mentioned above.

[0101] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0102] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0103] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0104] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0105] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for detecting and diagnosing secondary power supply faults in high temperature aging test equipment, characterized in that: include: By real-time monitoring of the secondary power supply of the target high-temperature aging test equipment, real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment is obtained; By setting test thresholds and 0 / 1 logic conversion rules for different fault types, the real-time monitoring data is constructed into a target one-dimensional test vector; Based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix, the fault type of the secondary power supply of the target high-temperature aging test equipment is determined and output.

2. The method according to claim 1, characterized in that Also includes: According to the types of electronic components in the secondary power supply of the high-temperature aging test equipment and the connection relationship between the electronic components, a simulation model of the secondary power supply of the high-temperature aging test equipment is constructed, and the simulation model of the secondary power supply of the high-temperature aging test equipment is used to obtain fault monitoring data of the secondary power supply of the high-temperature aging test equipment under different fault types; A data-driven fault diagnosis model is established, and the data-driven fault diagnosis model is trained using the fault monitoring data of the secondary power supply of the high-temperature aging test equipment under different fault types to obtain a trained data-driven fault diagnosis model.

3. The method according to claim 2, characterized in that Also includes: By injecting real fault data into the simulation model of the secondary power supply of the high temperature aging test equipment, fault monitoring data of the secondary power supply of the high temperature aging test equipment under different fault types are obtained; According to the set test threshold and the first 0 / 1 logic conversion rule, the fault monitoring data under the different fault types are respectively converted into the first 0 / 1 logic value; Based on the first 0 / 1 logic value, a physical one-dimensional test vector under different fault types is obtained, and an original D matrix is ​​constructed according to the physical one-dimensional test vector under the different fault types.

4. The method according to claim 3, characterized in that Also includes: The fault diagnosis results under different fault types are obtained by respectively inputting the fault monitoring data of the secondary power supply of the high temperature aging test equipment under different fault types into the trained data-driven fault diagnosis model; According to the set second 0 / 1 logic conversion rule, the fault diagnosis results under the different fault types are respectively converted into second 0 / 1 logic values; Based on the second 0 / 1 logical value, virtual one-dimensional test vectors under different fault types are obtained, and the virtual one-dimensional test vectors under different fault types are added to the original D matrix to obtain an extended D matrix; wherein the extended D matrix contains different fault types and their corresponding one-dimensional test vectors, and the one-dimensional test vectors are composed of physical one-dimensional test vectors and virtual one-dimensional test vectors.

5. The method according to claim 4, characterized in that The 0 / 1 logic conversion rule includes the first 0 / 1 logic conversion rule and the second 0 / 1 logic conversion rule, wherein the real-time monitoring data is constructed into a target one-dimensional test vector by setting test thresholds and 0 / 1 logic conversion rules for different fault types, including: By comparing the real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment with the test thresholds set for different fault types, a comparison result is obtained, and based on the comparison result and the first 0 / 1 logic conversion rule, a physical one-dimensional test vector is obtained; By inputting the real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment into the data-driven fault diagnosis model, a fault diagnosis result of the secondary power supply of the target high-temperature aging test equipment is obtained, and based on the fault diagnosis result and the second 0 / 1 logic conversion rule, a virtual one-dimensional test vector is obtained; Based on the physical one-dimensional test vector and the virtual one-dimensional test vector, a target one-dimensional test vector of the secondary power supply of the target high-temperature aging test equipment is obtained.

6. The method according to claim 5, characterized in that Based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix, the fault type of the secondary power supply of the target high-temperature aging test equipment is determined and output including: Calculating the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix; The maximum value of the similarities between the target one-dimensional test vector and the one-dimensional test vectors corresponding to the multiple fault types is determined, and the fault type corresponding to the maximum value is determined as the fault type of the secondary power supply of the target high-temperature aging test equipment and outputted.

7. The method according to claim 6, characterized in that Calculating the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix includes: Respectively calculating the Mahalanobis distance, cosine similarity and Manhattan distance between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix; By performing multi-distance metric fusion on the Mahalanobis distance, the cosine similarity and the Manhattan distance, the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix is ​​obtained, which includes: ； in, represents the similarity between the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the extended D matrix, represents the Mahalanobis distance; represents cosine similarity; represents Manhattan distance; , and denote weights, and + + =1.

8. A secondary power supply fault detection and diagnosis device for high temperature aging test equipment, comprising: An acquisition module is configured to obtain real-time monitoring data of the secondary power supply of the target high-temperature aging test equipment by real-time monitoring of the secondary power supply of the target high-temperature aging test equipment; A construction module is configured to construct the real-time monitoring data into a target one-dimensional test vector by setting test thresholds and 0 / 1 logic conversion rules for different fault types; The fault detection and diagnosis module is configured to determine and output the fault type of the secondary power supply of the target high-temperature aging test equipment based on the target one-dimensional test vector and the one-dimensional test vector corresponding to each fault type in the pre-stored extended D matrix.

9. A computing device comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the secondary power supply fault detection and diagnosis method of high temperature aging test equipment described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the secondary power supply fault detection and diagnosis method for high-temperature aging test equipment according to any one of claims 1 to 7.

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