Accelerated test and life evaluation method, device and equipment based on integration of data, theory and intelligence

Abstract

The invention relates to a method, a device and equipment for accelerated test and life evaluation based on integration of data, theory and intelligence. The method comprises the following steps: under the condition that different component devices of a to-be-tested product are placed in different acceleration test boxes and different acceleration stresses are respectively applied to the different acceleration test boxes, obtaining test detection data of each component device according to a failure type of each component device; according to the data type of each piece of test detection data, performing data analysis on each piece of test detection data to obtain a life distribution function of each piece of component equipment; and determining a service life evaluation test result of the to-be-tested product according to the failure relevance between the component devices and the service life distribution function of the component devices. The method can improve the accuracy of the life evaluation result.

Description

Technical Field

[0001] The present application relates to the technical field of product life assessment, and in particular to a method, device and equipment for accelerated testing and life assessment that integrates mathematical and rational analysis. Background Art

[0002] As industrial products become more complex and the market's reliability requirements become increasingly stringent, accelerated testing is often used to evaluate product life.

[0003] In related technologies, the entire product is usually placed in the same test box, the same stress is applied to all devices in the product, and the same detection method and the same data analysis method are used to achieve accelerated testing and life assessment of the product.

[0004] However, the life assessment method in the related art has a technical problem of low accuracy of the assessment results. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device and equipment for accelerated testing and life assessment that integrates mathematical and rational factors to improve the accuracy of life assessment results in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for accelerated testing and life assessment that integrates mathematical and rational analysis, including:

[0007] When different components of the product to be tested are placed in different accelerated test chambers, and different accelerated stresses are applied to the different accelerated test chambers, test data of each component device is obtained according to the failure type of each component device;

[0008] According to the data type of each test data, data analysis is performed on each test data to obtain the life distribution function of each component equipment;

[0009] The life evaluation test results of the product to be tested are determined based on the failure correlation between the component devices and the life distribution function of each component device.

[0010] In one embodiment, obtaining test data of each component device according to the failure type of each component device includes:

[0011] According to the failure type of each component equipment, obtain the inspection data acquisition requirements of each component equipment;

[0012] According to each test data acquisition requirement, in the mapping relationship between the test data acquisition requirement and the test detection method, a test detection method matching each test data acquisition requirement is acquired;

[0013] Adopt the test and detection method that matches the requirements for obtaining each test data, test each component equipment, and obtain the test and detection data of each component equipment.

[0014] In one embodiment, when the component device is a hard failure device, the data type includes failure time data; according to the data type of each test detection data, data analysis is performed on each test detection data to obtain the life distribution function of each component device, including:

[0015] Determine the failure likelihood function of the hard failure device according to the number of failed devices in the hard failure device, the failure time data of each failed device, and the mathematical statistics model of the hard failure device;

[0016] According to the failure likelihood function, the life distribution function of hard failure equipment is determined.

[0017] In one embodiment, when the component device is a first soft failure device, the data type includes first performance degradation data; and according to the data type of each test detection data, data analysis is performed on each test detection data to obtain the life distribution function of each component device, including:

[0018] Acquire a detection time series and a detection value series of a performance parameter of a first soft failure device according to the first performance degradation data;

[0019] According to the detection time series and the detection value series, the preset neural network is trained to obtain a performance detection value prediction model;

[0020] According to the performance detection value prediction model, the life distribution function of the first soft failure device is determined.

[0021] In one embodiment, determining the life distribution function of the first soft failure device according to the performance detection value prediction model includes:

[0022] Inputting the candidate time series into the performance test value prediction model to obtain the test value prediction sequence output by the performance test value prediction model;

[0023] Determine the failure time of the first soft failure device according to the candidate time series and the detection value prediction series;

[0024] According to the failure time, the life distribution function of the first soft failure device is determined.

[0025] In one embodiment, determining the life distribution function of the first soft failure device according to the failure time includes:

[0026] Determining a life assessment parameter of the first soft failure device according to the device quantity and failure time of the first soft failure device;

[0027] The life distribution function of the first soft failure device is determined according to the life evaluation parameter.

[0028] In one embodiment, when the component device is a second soft failure device, the data type includes second performance degradation data; the amount of the second performance degradation data is less than the amount of the first performance degradation data; and according to the data type of each test detection data, data analysis is performed on each test detection data to obtain the life distribution function of each component device, including:

[0029] Acquire a detection value sequence of a performance parameter of a second soft failure device according to the second performance degradation data;

[0030] Determining a failure time of the second soft failure device according to a failure physical model and a detection value sequence of the second soft failure device;

[0031] According to the failure time, the life distribution function of the second soft failure device is determined.

[0032] In one embodiment, determining the life distribution function of the second soft failure device according to the failure time includes:

[0033] Determining life assessment parameters of the second soft failure device according to the device quantity and failure time of the second soft failure device;

[0034] The life distribution function of the second soft failure device is determined according to the life evaluation parameter.

[0035] In one embodiment, determining the life evaluation test result of the product to be tested according to the failure correlation between the component devices and the life distribution function of the component devices includes:

[0036] Determine the life distribution function of the product to be tested based on the failure correlation between the components and the life distribution function of the components;

[0037] The life evaluation test result of the product to be tested is determined based on the acceleration factor of the product to be tested under accelerated stress and the life distribution function of the product to be tested.

[0038] In a second aspect, the present application also provides a mathematical and rational fusion accelerated testing and life assessment device, comprising:

[0039] A data acquisition module is used to obtain test data of each component device according to the failure type of each component device when different component devices of the product to be tested are placed in different accelerated test chambers and different accelerated stresses are applied to the different accelerated test chambers respectively;

[0040] A data analysis module is used to analyze each test data according to the data type of each test data to obtain the life distribution function of each component equipment;

[0041] The result determination module is used to determine the life evaluation test result of the product to be tested according to the failure correlation between the component devices and the life distribution function of the component devices.

[0042] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the embodiments of the first aspect are implemented.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any one of the embodiments of the first aspect above.

[0044] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method in any one of the embodiments of the first aspect above.

[0045] The accelerated test and life assessment method, device and equipment of the embodiment of the present application provided by the mathematical and rational fusion, when different components of the product to be tested are placed in different accelerated test boxes, and different accelerated test boxes are respectively applied with different accelerated stresses, according to the failure type of each component device, the test detection data of each component device is obtained, and then according to the data type of each test detection data, the test detection data is analyzed to obtain the life distribution function of each component device, and then according to the failure correlation between each component device and the life distribution function of each component device, the life assessment test result of the product to be tested is determined. In this method, when the life assessment test of the product to be tested is performed, first, the different components of the product to be tested are placed in different accelerated test boxes, and different accelerated stresses are applied to the different accelerated test boxes. On this basis, based on the failure type of each component device, different detection methods are used to detect each component device to obtain the test detection data of each component device, and then based on the data type of the test detection data, different data analysis methods are used to analyze each test detection data to obtain the life distribution function of each component device, so as to obtain the life assessment result of the product to be tested based on the life distribution function of each component device. During this process, stress application, equipment testing and data analysis are all performed on different component equipment according to their types, making the life assessment of each component equipment more precise, thereby improving the accuracy of product life assessment; at the same time, the failure correlation between different component equipment is also considered during the life assessment, further improving the accuracy of the life assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 is an internal structure diagram of a computer device in one embodiment;

[0048] Figure 2 A schematic diagram of a flow chart of a method for accelerating a test and lifespan assessment by integrating mathematical reasoning in one embodiment;

[0049] Figure 3 is a schematic diagram of stress application in one embodiment;

[0050] Figure 4 A schematic diagram of a process for obtaining test data in an embodiment;

[0051] Figure 5 A schematic diagram of an experimental test in one embodiment;

[0052] Figure 6 A schematic diagram of a process for determining a life distribution function in one embodiment;

[0053] Figure 7 A schematic diagram of a process for determining a life distribution function in another embodiment;

[0054] Figure 8 A schematic diagram of a process for determining a life distribution function in another embodiment;

[0055] Fig. 9 A schematic diagram of a process for determining a life distribution function in another embodiment;

[0056] Fig.10 A schematic diagram of a process for determining a life assessment test result in one embodiment;

[0057] Fig.11 Schematic diagram of the structure of a mathematical and rational fusion accelerated testing and life assessment device in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] The technical background of the embodiments of the present application is first described below.

[0060] Accelerated testing is a test method that stimulates the potential failure mechanism of a product and shortens its failure time by artificially applying stress (such as temperature, voltage, mechanical load, etc.) that is higher than normal use conditions. Its core purpose is to simulate the performance degradation or failure process of a product in long-term actual use in a relatively short period of time, so as to quickly evaluate the life of the product. Accelerated testing can compress the traditional life test that takes several years to complete into a few months or weeks. Therefore, it is widely used in the life assessment of high-reliability products.

[0061] The traditional accelerated test and life assessment method is to put the whole product into the same test chamber, apply the same accelerated stress, implement the same detection method, and use a single assessment method to achieve the accelerated test and life assessment of the product. However, the traditional accelerated test method is not targeted, resulting in low accuracy of life assessment.

[0062] Based on this, the embodiment of the present application provides a method for accelerated testing and life assessment that integrates mathematical reasoning. When conducting a life assessment test on a product to be tested, first, different components of the product to be tested are placed in different accelerated test boxes, and different accelerated stresses are applied to different accelerated test boxes. On this basis, based on the failure type of each component device, different detection methods are used to detect each component device, and the test detection data of each component device is obtained. Then, based on the data type of the test detection data, different data analysis methods are used to analyze each test detection data, and the life distribution function of each component device is obtained, thereby obtaining the life assessment result of the product to be tested based on the life distribution function of each component device. In this process, stress application, equipment detection and data analysis are all operated on different components by type, so that the life assessment of each component device is more accurate, thereby improving the accuracy of the product life assessment; at the same time, the failure correlation between different components is also considered during the life assessment, further improving the accuracy of the life assessment result. Of course, the technical solution provided in the embodiment of the present application is not limited to solving only the above problems, and there are other technical effects, which can be specifically referred to in the following embodiment description.

[0063] It should be noted that the beneficial effects brought about by the embodiments of the present application or the technical problems solved are not limited to this one, but may also include other implicit or related problems. For details, please refer to the description of the following embodiments.

[0064] The following mathematical and rational fusion accelerated test and life assessment method provided in the embodiment of the present application can be applied to computer equipment. The computer equipment can be a server, and its internal structure diagram can be as follows: Figure 1As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a mathematical and rational fusion accelerated test and life assessment method. Those skilled in the art can understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0065] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0066] In an exemplary embodiment, Figure 2 As shown in the figure, a mathematical and rational fusion accelerated test and life assessment method is provided. Figure 1 The computer device in the example is used to illustrate, including the following steps 201 to 203. Among them:

[0067] S201, when different components of the product to be tested are placed in different accelerated test chambers and different accelerated stresses are applied to the different accelerated test chambers, test detection data of each component device is obtained according to the failure type of each component device.

[0068] In the embodiments of the present application, the constituent devices of the product to be tested are divided into hard failure devices and soft failure devices according to the failure type. Hard failure refers to the sudden and complete loss of the specified function of the equipment at a certain moment, which is usually unpredictable and instantaneous. This failure mode is usually caused by a sudden external shock or internal failure. Soft failure refers to the gradual decline in the function or performance of the equipment over time, and eventually reaches a state where it cannot work normally. This failure mode is usually caused by factors such as long-term wear, corrosion, fatigue or aging. At the same time, soft failure devices are further divided into soft failure devices with unknown failure physical models (Class A soft failure devices) and soft failure devices with known failure physical models (Class B soft failure devices).

[0069] When conducting an accelerated test on a product to be tested, first place the components of the product to be tested into different accelerated test chambers and apply different sensitive stress types and stress values. Figure 3 As shown, the components of the product to be tested include hard failure equipment, type A soft failure equipment, and type B soft failure equipment. The sensitive stress types of hard failure equipment are mostly mechanical load stress, etc., and the accelerated test stress is mechanical load stress, etc. The sensitive stress types of soft failure equipment are mostly temperature and humidity stress, etc., and the accelerated test stress is temperature and humidity stress, etc. And different components are interconnected, and accelerated tests are carried out at the same time. It should be noted that there are multiple products to be tested, and the same type of components of each product to be tested are placed in the same accelerated test box.

[0070] After placing different components of the product to be tested into different accelerated test chambers and applying different sensitive stress types and stress values ​​to the different accelerated test chambers, the different components are tested to obtain test data of each component.

[0071] In actual application, based on the failure type of each component device, a detection method matching each failure type can be obtained, and each component device can be tested using the detection method matching each failure type to obtain test detection data of each component device. For example, a mapping relationship between failure types and detection methods can be obtained from a database, and then, based on the failure type of each component device, a detection method corresponding to each failure type can be matched from the mapping relationship.

[0072] S202, performing data analysis on each test data according to the data type of each test data to obtain the life distribution function of each component equipment.

[0073] After obtaining the test data of each component equipment, it is necessary to perform data analysis on each test data to obtain the life distribution function of each component equipment.

[0074] In the embodiment of the present application, when performing data analysis on each test detection data, based on the data type of each test detection data, a data analysis method matching each data type is obtained, and the data analysis method matching each data type is used to perform data analysis on the test detection data of each component device, respectively, to obtain the life distribution function of each component device. For example, the data analysis method includes mathematical statistics method, artificial intelligence algorithm and failure physics model, etc. Different test detection data correspond to different data analysis methods. Based on the test detection data, a data analysis method suitable for the test detection data is selected, and based on the selected data analysis method, combined with the test detection data, the life distribution function of each component device is determined.

[0075] S203, determining a life evaluation test result of the product to be tested according to the failure correlation between the component devices and the life distribution function of the component devices.

[0076] The failure correlation between the component devices refers to the related competitive failure relationship between the component devices, that is, the failures between the devices are competitive and correlated.

[0077] Based on the life distribution function of each component device obtained above, combined with the failure correlation between each component device, the life of the product to be tested is evaluated to obtain the life evaluation test result of the product to be tested. For example, the failure correlation between each component device is integrated with the life distribution function of each component device to obtain the life distribution function of the product to be tested, and the life distribution function of the product to be tested is analyzed to obtain the life evaluation test result of the product to be tested.

[0078] In the accelerated test and life assessment method of mathematical rational fusion provided in the embodiment of the present application, when different components of the product to be tested are placed in different accelerated test boxes, and different accelerated test boxes are respectively applied with different accelerated stresses, according to the failure type of each component device, the test detection data of each component device is obtained, and then according to the data type of each test detection data, the test detection data is analyzed to obtain the life distribution function of each component device, and then according to the failure correlation between each component device and the life distribution function of each component device, the life assessment test result of the product to be tested is determined. In this method, when the product to be tested is tested for life assessment, first, different components of the product to be tested are placed in different accelerated test boxes, and different accelerated stresses are applied to different accelerated test boxes. On this basis, based on the failure type of each component device, different detection methods are used to detect each component device to obtain the test detection data of each component device, and then based on the data type of the test detection data, different data analysis methods are used to analyze each test detection data to obtain the life distribution function of each component device, so as to obtain the life assessment result of the product to be tested based on the life distribution function of each component device. During this process, stress application, equipment testing and data analysis are all performed on different component equipment according to their types, making the life assessment of each component equipment more precise, thereby improving the accuracy of product life assessment; at the same time, the failure correlation between different component equipment is also considered during the life assessment, further improving the accuracy of the life assessment results.

[0079] Based on the above embodiment, an embodiment is provided to illustrate the process of obtaining the test detection data.

[0080] In an exemplary embodiment, Figure 4 As shown, according to the failure type of each component equipment, the test data of each component equipment is obtained, including:

[0081] S301, obtaining the detection data acquisition requirement of each component device according to the failure type of each component device.

[0082] The failure types of each component device include hard failure devices, soft failure devices with unknown failure physical models, and soft failure devices with known failure physical models.

[0083] Different failure types have different detection data acquisition requirements. For hard failure devices, the detection data acquisition requirement is to obtain the accurate failure time of the device; for soft failure devices with unknown failure physical models, the detection data acquisition requirement is to obtain a large amount of performance degradation data; for soft failure devices with known failure physical models, the detection data acquisition requirement is to obtain a small amount of performance degradation data.

[0084] S302 , according to each test data acquisition requirement, in the mapping relationship between the test data acquisition requirement and the test detection method, obtain the test detection method matching each test data acquisition requirement.

[0085] The database stores the mapping relationship between different test data acquisition requirements and different test detection methods. When testing each component device, the test data acquisition requirements of each component device can be first matched with the test detection method corresponding to each test data acquisition requirement from the mapping relationship.

[0086] Among them, the test data acquisition requirement is to obtain the accurate failure time of the equipment, and the corresponding test detection method is real-time online detection to obtain the specific failure time. The test data acquisition requirement is to obtain a large amount of performance degradation data, and the corresponding test detection method is offline regular detection with a short time interval. The test data acquisition requirement is a small amount of performance degradation data, and the corresponding test detection method is offline regular detection with a long time interval. The test detection methods corresponding to different components of the equipment are as follows: Figure 5 shown.

[0087] S303, using a test detection method that matches the requirements for obtaining each test data, to detect each component device and obtain test detection data of each component device.

[0088] After obtaining the test detection methods of each component device, each test detection method is used to detect the corresponding component devices respectively, and the test detection data of each component device is obtained. Specifically, for hard failure devices, real-time online detection equipment is used to perform real-time online detection on each hard failure device; for Class A soft failure devices, offline detection equipment is used to perform offline detection on each Class A soft failure device, and the detection time interval is short; for Class B soft failure devices, offline detection equipment is used to perform offline detection on each Class B soft failure device, and the detection time interval is long.

[0089] In the accelerated test and life assessment method of mathematical and rational fusion provided in the embodiment of the present application, firstly, according to the failure type of each component device, the detection data acquisition requirements of each component device are obtained, and then according to each detection data acquisition requirement, in the mapping relationship between the detection data acquisition requirements and the test detection method, the test detection method matching the detection data acquisition requirements is obtained, and then the test detection method matching the detection data acquisition requirements is adopted to test each component device and obtain the test detection data of each component device. In this method, firstly, based on the failure type of different component devices, the detection data acquisition requirements of each component device are obtained, and different detection data requirements correspond to different test detection methods. Based on this, the test detection method of each component device is obtained from the mapping relationship between the detection data acquisition requirements and the test detection method, so that the corresponding component device is tested based on the test detection method of each component device, and the test detection data of each component device is obtained, which improves the accuracy of the detection data and thus improves the accuracy of the life assessment.

[0090] Based on the above embodiment, an embodiment is provided to illustrate the process of determining the lifetime distribution function.

[0091] In an exemplary embodiment, Figure 6 As shown, when the component equipment is a hard failure equipment, the data type includes failure time data; according to the data type of each test detection data, data analysis is performed on each test detection data to obtain the life distribution function of each component equipment, including:

[0092] S401, determining a failure likelihood function of a hard failure device according to the number of failed devices in the hard failure device, the failure time data of each failed device, and a mathematical statistical model of the hard failure device.

[0093] In the embodiments of the present application, the accelerated test data analysis of the hard failure equipment adopts mathematical statistics.

[0094] For n hard failure devices, assuming a total of k ( ) hard failure devices fail, then the failure times of the hard failure devices are sorted from small to large. Assuming that the life distribution function of hard failure equipment is log-normal distribution, the mathematical statistical model of hard failure equipment is as follows (1).

[0095] (1)

[0096] in, is the life distribution function of hard failure equipment; and is the life assessment parameter; n is the number of products to be tested, and is also the number of hard failure devices, Class A soft failure devices, and Class B soft failure devices.

[0097] According to the life distribution function of hard failure equipment, the failure likelihood function of hard failure equipment is obtained as follows:

[0098] (2)

[0099] Where S is the failure likelihood function and t0 is the cut-off time of the accelerated test.

[0100] S402: Determine a life distribution function of a hard failure device according to a failure likelihood function.

[0101] By solving the following equation group (3), the life assessment parameters of hard failure equipment are obtained: and , thus obtaining the life distribution function of hard failure equipment.

[0102] (3)

[0103] In the accelerated test and life assessment method of mathematical fusion provided in the embodiment of the present application, the failure likelihood function of the hard failure device is first determined based on the number of failed devices in the hard failure device, the failure time data of each failed device, and the mathematical statistics model of the hard failure device, and then the life distribution function of the hard failure device is determined based on the failure likelihood function. In this method, the data type of the test detection data of the hard failure device is failure time data, and there is no need to predict the failure time. The failure time data of each component device is analyzed by mathematical statistics to obtain the life distribution function of the hard failure device. By using a data analysis method suitable for hard failure device detection data, the hard failure device is analyzed, and the accuracy of life assessment of the hard failure device is improved.

[0104] Based on the above embodiment, another embodiment is provided to illustrate the process of determining the lifetime distribution function.

[0105] In an exemplary embodiment, Figure 7 As shown, when the component device is a first soft failure device, the data type includes first performance degradation data; according to the data type of each test detection data, data analysis is performed on each test detection data to obtain the life distribution function of each component device, including:

[0106] S501: Acquire a detection time series and a detection value series of a performance parameter of a first soft failure device according to first performance degradation data.

[0107] In the embodiment of the present application, the first soft failure device is a Class A soft failure device, and the accelerated test data analysis of the Class A soft failure device adopts an artificial intelligence algorithm. The first performance degradation data is a large amount of performance degradation data obtained by testing the Class A soft failure device using an offline detection device.

[0108] For n Class A soft failure devices, assuming a total of The detection times are , the detection values ​​of the performance parameters of n Class A soft failure devices are obtained as shown in Table 1 below.

[0109] Table 1

[0110]

[0111] Among them, the detection time series The test value sequence x is shown in the following formula (4).

[0112] (4)

[0113] S502, training a preset neural network according to the detection time series and the detection value series to obtain a performance detection value prediction model.

[0114] After obtaining the detection time series and detection value series of Class A soft failure devices, the detection time and detection value are normalized to obtain the normalized detection time series and normalized detection value series, and then the normalized detection time series and normalized detection value series are used as training data of the neural network for model training. The normalized detection time series is input into the neural network to obtain the predicted detection value output by the neural network, and the loss is calculated based on the predicted detection value and the normalized detection value series, and the model parameters of the neural network are continuously adjusted based on the calculated loss value until the training is completed to obtain a performance prediction model.

[0115] Among them, the normalization formula of the detection time and the normalization formula of the detection value of the performance parameter are as follows: Formula (5) and (6).

[0116] (5)

[0117] in, for Normalized value; is the jth detection time; To obtain The minimum value of To obtain The maximum value of .

[0118] (6)

[0119] in, for Normalized value; is the j-th detection value of the i-th type A soft failure device; To obtain The minimum value of To obtain The maximum value of .

[0120] Thus, the normalized detection time series is obtained And the normalized detection value sequence .

[0121] (7)

[0122] Artificial intelligence algorithm is used to normalize the detection time series And the normalized detection value sequence Conduct training to build an artificial intelligence model for Class A soft failure devices.

[0123] S503: Determine a life distribution function of the first soft failure device according to the performance detection value prediction model.

[0124] Based on the performance test value prediction model obtained through the above training, the test values ​​at other subsequent times are predicted, so as to determine the life distribution function of the first soft failure device based on the predicted test values.

[0125] In one embodiment, if Figure 8 As shown, according to the performance detection value prediction model, determining the life distribution function of the first soft failure device includes the following steps:

[0126] S601, inputting the candidate time series into the performance prediction model to obtain the detection value prediction sequence output by the performance detection value prediction model.

[0127] The candidate time series Input the performance test value prediction model to obtain the test value prediction sequence .

[0128] (8)

[0129] S602: Determine the failure time of the first soft failure device according to the candidate time series and the detection value prediction series.

[0130] After obtaining the detection value prediction sequence, the candidate time series and the detection value prediction sequence are respectively inverse normalized to obtain the inverse normalized candidate time series and the inverse normalized detection value prediction sequence, and then based on the inverse normalized candidate time series and the inverse normalized detection value prediction sequence, the failure time of the first soft failure device is predicted.

[0131] The inverse normalization formula of the candidate time series is: .in, for The inverse normalized value. The inverse normalization formula for the test value prediction sequence is: .in, for The inverse normalized value.

[0132] Thus, the candidate time series after inverse normalization is obtained And the detection value prediction sequence after inverse normalization As shown in formula (9).

[0133] (9)

[0134] Based on the candidate time series obtained after inverse normalization And the detection value prediction sequence after inverse normalization , combined with the failure threshold of Class A soft failure devices, the failure time is evaluated, and the failure times of n Class A soft failure devices are obtained as .

[0135] Exemplarily, each predicted detection value in the detection value prediction sequence is compared with the failure threshold of the Class A soft failure device to obtain a predicted detection value greater than the failure threshold, and the candidate time corresponding to the predicted detection value greater than the failure threshold is determined as the failure time of the first soft failure device.

[0136] S603: Determine a life distribution function of the first soft failure device according to the failure time.

[0137] Based on the estimated failure time and in combination with the life assessment parameter of the first soft failure device, a life distribution function of the first soft failure device is determined.

[0138] Exemplarily, based on the number of devices and failure time of the first soft failure devices, life assessment parameters of the first soft failure devices are determined; based on the life assessment parameters, a life distribution function of the first soft failure devices is determined.

[0139] Assuming that the life distribution function of type A soft failure equipment is exponential distribution, its life distribution function is as shown in the following formula (10).

[0140] (10)

[0141] in, is the life distribution function of Class A soft failure equipment, It is the life assessment parameter of Class A soft failure equipment.

[0142] By solving the following equation (11), the life assessment parameters of Class A soft failure devices are evaluated: .

[0143] (11)

[0144] Thus, the life assessment parameters to be solved are Substituting into the above formula (10), the life distribution function of the first soft failure device is obtained: .

[0145] In the accelerated test and life assessment method of mathematical and rational fusion provided in the embodiment of the present application, firstly, according to the first performance degradation data, the detection time series and detection value series of the performance parameters of the first soft failure device are obtained, and then according to the detection time series and detection value series, the preset neural network is trained to obtain the performance detection value prediction model, and then according to the performance detection value prediction model, the life distribution function of the first soft failure device is determined. In this method, the data type of the test detection data of the first soft failure device is a large amount of performance degradation data, and the failure physical model of the first soft failure device is unknown, and it is necessary to use an artificial intelligence algorithm to predict the failure time. Therefore, after obtaining the detection time series and detection value series of the first soft failure device, they are used as training data for model training, so as to determine the life distribution function of the first soft failure device based on the trained model, and by adopting a data analysis method suitable for the detection data of the first soft failure device, the detection data of the first soft failure device is analyzed, thereby improving the accuracy of the life assessment of the first soft failure device.

[0146] Based on the above embodiment, another embodiment is provided to illustrate the process of determining the lifetime distribution function.

[0147] In an exemplary embodiment, Fig. 9 As shown, when the component device is a second soft failure device, the data type includes second performance degradation data; the amount of the second performance degradation data is less than the amount of the first performance degradation data; according to the data type of each test detection data, each test detection data is analyzed to obtain the life distribution function of each component device, including:

[0148] S701: Acquire a detection value sequence of a performance parameter of a second soft failure device according to second performance degradation data.

[0149] In the embodiment of the present application, the second soft failure device is a Class B soft failure device, and the accelerated test data analysis of the Class B soft failure device adopts a failure physical model. The second performance degradation data is a small amount of performance degradation data obtained by testing the Class B soft failure device using an offline detection device.

[0150] For n Class B soft failure devices, assuming a total of The detection times are ,get The test values ​​of the performance parameters of the Class B soft failure devices are shown in Table 2 below.

[0151] Table 2

[0152]

[0153] S702: Determine a failure time of the second soft failure device according to a failure physical model and a detection value sequence of the second soft failure device.

[0154] The failure physical models of different Class B soft failure devices are different. For example, the main failure mechanism of a board is the metal electromigration failure of microwave power devices, and its failure physical model is: , where t is the failure time; J is the current density of the microwave power device of the plate, obtained by detection; is the threshold value of current density, obtained from the device manual; E is the activation energy, obtained from the device manual; is the model parameter obtained from engineering experience; b is the Boltzmann constant; W is the temperature stress value of the accelerated test.

[0155] The current density (detection value) obtained by the test is brought into the above failure physical model to evaluate the failure time of the Class B soft failure device. The failure time of the Class B soft failure devices is .

[0156] S703: Determine a life distribution function of a second soft failure device according to the failure time.

[0157] Based on the estimated failure time and in combination with the life assessment parameter of the second soft failure device, a life distribution function of the second soft failure device is determined.

[0158] Exemplarily, based on the number of devices and failure time of the second soft failure devices, life assessment parameters of the second soft failure devices are determined; based on the life assessment parameters, life distribution function of the second soft failure devices is determined.

[0159] Similarly, assuming that the life distribution function of Class B soft failure equipment is exponential distribution, its life distribution function is as shown in the following formula (12).

[0160] (12)

[0161] in, is the life distribution function of Class B soft failure equipment; Life assessment parameters for Class B soft failure devices.

[0162] By solving the following equation (13), the life assessment parameters of Class B soft failure devices are evaluated: .

[0163] (13)

[0164] Thus, the life assessment parameters to be solved are Substituting into the above formula (12), the life distribution function of the second soft failure device is obtained: .

[0165] In the accelerated test and life assessment method of mathematical and rational fusion provided in the embodiment of the present application, first, according to the second performance degradation data, the detection value sequence of the performance parameter of the second soft failure device is obtained, and then according to the failure physical model and the detection value sequence of the second soft failure device, the failure time of the second soft failure device is determined, and then according to the failure time, the life distribution function of the second soft failure device is determined. In this method, the data type of the test detection data of the second soft failure device is a small amount of performance degradation data, and the failure physical model of the second soft failure device is known, then the failure physical model can be directly used to predict the failure time, so after obtaining the detection value sequence of the second soft failure device, the failure time is estimated in combination with the failure physical model of the second soft failure device, so as to determine the life distribution function of the first soft failure device based on the estimated failure time, and by adopting a data analysis method suitable for the detection data of the second soft failure device, the detection data of the first soft failure device is analyzed, thereby improving the accuracy of the life assessment of the second soft failure device.

[0166] Based on the above embodiment, an embodiment is provided to illustrate the process of determining the life evaluation test result.

[0167] In an exemplary embodiment, Fig.10 As shown, according to the failure correlation between the components and the life distribution function of each component, the life evaluation test results of the product to be tested are determined, including:

[0168] S801, determining the life distribution function of the product to be tested according to the failure correlation between the component devices and the life distribution function of the component devices.

[0169] In the embodiment of the present application, the related competitive failure relationship between hard failure devices, type A failure devices and type B failure devices is considered, that is, the failures between devices are competitive and correlated, so as to construct a related competitive failure model of the product to be tested, that is, the life distribution function of the product to be tested, as shown in the following formula (14).

[0170] (14)

[0171] in, is the life distribution function of the product to be tested; for The correlation function of are the unknown parameters of the correlation function, which are evaluated by fitting the data.

[0172] Furthermore, the life distribution function of the product to be tested is for

[0173] .

[0174] S802, determining a life evaluation test result of the product to be tested according to an acceleration factor of the product to be tested under accelerated stress and a life distribution function of the product to be tested.

[0175] Average life of the product under test under accelerated stress for

[0176] .

[0177] Assuming that the acceleration factor of the product under test is A, the average life T of the product under normal working stress is

[0178] .

[0179] The average life of the product under normal working stress obtained above is the life evaluation test result of the product under test.

[0180] In the accelerated test and life assessment method of mathematical rational fusion provided in the embodiment of the present application, the life distribution function of the product to be tested is first determined based on the failure correlation between the component devices and the life distribution function of the component devices, and then the life assessment test result of the product to be tested is determined based on the acceleration factor of the product to be tested under accelerated stress and the life distribution function of the product to be tested. In this method, when performing life assessment on the product to be tested, the relevant competitive failure relationship between different component devices is considered to construct the life distribution function of the product to be tested, and then the life assessment test result of the product to be tested is obtained in combination with the acceleration factor of the product to be tested under accelerated stress. By additionally considering the competitive correlation between different component devices in the product, the accuracy of product life assessment is improved.

[0181] In addition, in an exemplary embodiment, the accelerated test and life assessment process in the embodiment of the present application are described.

[0182] S1. Place the components of the product into different accelerated test chambers and apply different sensitive stress types and stress values.

[0183] The sensitive stress types of hard failure equipment are mostly mechanical load stress, etc., and the accelerated test stress is mechanical load stress, etc. The sensitive stress types of soft failure equipment are mostly temperature and humidity stress, etc., and the accelerated test stress is temperature and humidity stress, etc. The different components of the equipment are interconnected, and the accelerated test is carried out at the same time.

[0184] S2. Different testing methods are implemented for different devices due to different testing data requirements.

[0185] For hard failure devices, the detection data requirement is to obtain the accurate failure time of the device, so it is necessary to implement real-time online detection to obtain the specific failure time. For soft failure devices (including Class A soft failure devices and Class B soft failure devices), the detection data requirement is to obtain the performance degradation data of the device for predicting the failure time, and it is not necessary to obtain the accurate failure time of the device. In order to save test costs, it is only necessary to implement offline regular detection. Further subdivided, for Class A soft failure devices, the detection data requirement is to obtain a large amount of performance degradation data and use intelligent algorithms to predict the failure time, so a shorter offline regular detection time interval is required. For Class B soft failure devices, the detection data requirement is to obtain a small amount of performance degradation data and use the failure physical model to predict the failure time. In order to save test costs, a longer offline regular detection time interval is required.

[0186] S3. For different component equipment, different data analysis methods are used to analyze the accelerated test data to obtain the life distribution function of each component equipment.

[0187] The accelerated test data analysis of hard failure equipment adopts mathematical statistics methods. The accelerated test data analysis of Class A soft failure equipment adopts artificial intelligence algorithms. The accelerated test data analysis of Class B soft failure equipment adopts failure physics models.

[0188] S4. Consider the relevant competitive failure relationship between hard failure devices, Class A soft failure devices and Class B soft failure devices, construct the product life distribution function, and combine the acceleration factor under the accelerated stress of the product to determine the average life of the product under normal working stress.

[0189] In this embodiment, a method for accelerated testing and life assessment that integrates mathematical reasoning with higher test efficiency, lower test cost, and higher assessment accuracy is provided, which can quickly and accurately assess the life of a product. The component equipment of the product is divided into hard failure equipment and soft failure equipment (including Class A soft failure equipment and Class B soft failure equipment) according to the failure type. Different equipment types are placed in different accelerated test chambers and different acceleration stresses are applied to achieve the maximum acceleration effect of the product accelerated test and improve the efficiency of the accelerated test. Different detection methods are implemented to reduce the cost of accelerated testing in view of the different requirements for test data of different equipment. Different evaluation methods are used in view of the characteristics of test and detection data of different equipment, and then a comprehensive evaluation is carried out to make the test data analysis more targeted and improve the accuracy of life assessment. Considering the competitiveness and correlation of failures between hard failure equipment, Class A soft failure equipment, and Class B soft failure equipment, a relevant competitive failure model of the product is constructed, and then the average life of the product is evaluated to improve the accuracy of life assessment.

[0190] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0191] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the above-mentioned accelerated test and life assessment method for implementing the accelerated test and life assessment method for implementing the mathematical and rational integration. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the accelerated test and life assessment device for implementing the mathematical and rational integration provided below can be referred to the above limitations on the accelerated test and life assessment method for implementing the mathematical and rational integration, and will not be repeated here.

[0192] In an exemplary embodiment, Fig.11 As shown, a mathematical and rational fusion accelerated test and life assessment device 1 is provided, comprising: a data acquisition module 10, a data analysis module 20 and a result determination module 30, wherein:

[0193] The data acquisition module 10 is used to obtain the test data of each component device according to the failure type of each component device when different component devices of the product to be tested are placed in different accelerated test chambers and different accelerated stresses are applied to the different accelerated test chambers respectively;

[0194] The data analysis module 20 is used to analyze the test data according to the data type of each test data to obtain the life distribution function of each component device;

[0195] The result determination module 30 is used to determine the life evaluation test result of the product to be tested according to the failure correlation between the component devices and the life distribution function of the component devices.

[0196] In one embodiment, the data acquisition module 10 is further used for:

[0197] According to the failure type of each component equipment, the test data acquisition requirement of each component equipment is obtained; according to each test data acquisition requirement, in the mapping relationship between the test data acquisition requirement and the test detection method, the test detection method matching each test data acquisition requirement is obtained; using the test detection method matching each test data acquisition requirement, each component equipment is tested to obtain the test detection data of each component equipment.

[0198] In one embodiment, the data analysis module 20 is further used for:

[0199] According to the number of failed devices in the hard failure devices, the failure time data of each failed device, and the mathematical statistics model of the hard failure devices, the failure likelihood function of the hard failure devices is determined; according to the failure likelihood function, the life distribution function of the hard failure devices is determined.

[0200] In one embodiment, the data analysis module 20 is further used for:

[0201] According to the first performance degradation data, a detection time series and a detection value series of the performance parameters of the first soft failure device are obtained; according to the detection time series and the detection value series, a preset neural network is trained to obtain a performance detection value prediction model; according to the performance detection value prediction model, a life distribution function of the first soft failure device is determined.

[0202] In one embodiment, the data analysis module 20 is further used for:

[0203] Determine the life distribution function of the first soft failure device according to the performance detection value prediction model, including:

[0204] The candidate time series is input into the performance detection value prediction model to obtain the detection value prediction sequence output by the performance detection value prediction model; the failure time of the first soft failure device is determined according to the candidate time series and the detection value prediction sequence; and the life distribution function of the first soft failure device is determined according to the failure time.

[0205] In one embodiment, the data analysis module 20 is further used for:

[0206] According to the device quantity and failure time of the first soft failure device, the life evaluation parameter of the first soft failure device is determined; according to the life evaluation parameter, the life distribution function of the first soft failure device is determined.

[0207] In one embodiment, the data analysis module 20 is further used for:

[0208] According to the second performance degradation data, a detection value sequence of the performance parameter of the second soft failure device is obtained; according to the failure physical model and the detection value sequence of the second soft failure device, the failure time of the second soft failure device is determined; according to the failure time, the life distribution function of the second soft failure device is determined.

[0209] In one embodiment, the data analysis module 20 is further used for:

[0210] According to the device quantity and failure time of the second soft failure device, the life evaluation parameter of the second soft failure device is determined; and according to the life evaluation parameter, the life distribution function of the second soft failure device is determined.

[0211] In one embodiment, the result determination module 30 is further used for:

[0212] The life distribution function of the product to be tested is determined based on the failure correlation between the component devices and the life distribution function of each component device; the life assessment test result of the product to be tested is determined based on the acceleration factor of the product to be tested under accelerated stress and the life distribution function of the product to be tested.

[0213] Each module in the above-mentioned accelerated test and life assessment device with mathematical and rational integration can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0214] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0215] When different components of the product to be tested are placed in different accelerated test chambers, and different accelerated stresses are applied to the different accelerated test chambers, test data of each component device is obtained according to the failure type of each component device;

[0216] According to the data type of each test data, data analysis is performed on each test data to obtain the life distribution function of each component equipment;

[0217] The life evaluation test results of the product to be tested are determined based on the failure correlation between the component devices and the life distribution function of each component device.

[0218] The implementation principles and technical effects of each step implemented by the processor in the embodiment of the present application are similar to the principles of the above-mentioned accelerated test and life assessment method with mathematical and rational fusion, and will not be repeated here.

[0219] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0220] When different components of the product to be tested are placed in different accelerated test chambers, and different accelerated stresses are applied to the different accelerated test chambers, test data of each component device is obtained according to the failure type of each component device;

[0221] According to the data type of each test data, data analysis is performed on each test data to obtain the life distribution function of each component equipment;

[0222] The life evaluation test results of the product to be tested are determined based on the failure correlation between the component devices and the life distribution function of each component device.

[0223] The implementation principles and technical effects of the various steps implemented when the computer program in the embodiment of the present application is executed by the processor are similar to the principles of the above-mentioned accelerated test and life assessment method integrating mathematical reasoning, and will not be repeated here.

[0224] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0225] When different components of the product to be tested are placed in different accelerated test chambers, and different accelerated stresses are applied to the different accelerated test chambers, test data of each component device is obtained according to the failure type of each component device;

[0226] According to the data type of each test data, data analysis is performed on each test data to obtain the life distribution function of each component equipment;

[0227] The life evaluation test results of the product to be tested are determined based on the failure correlation between the component devices and the life distribution function of each component device.

[0228] The implementation principles and technical effects of the various steps implemented when the computer program in the embodiment of the present application is executed by the processor are similar to the principles of the above-mentioned accelerated test and life assessment method integrating mathematical reasoning, and will not be repeated here.

[0229] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0230] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0231] The various technical types in the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical types in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical types, they should be considered to be within the scope of this specification.

[0232] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A mathematical and rational fusion accelerated test and life assessment method, characterized in that: The method comprises: When different components of the product to be tested are placed in different accelerated test chambers, and different accelerated stresses are applied to the different accelerated test chambers, test detection data of each component device is obtained according to the failure type of each component device; According to the data type of each of the test and detection data, data analysis is performed on each of the test and detection data to obtain the life distribution function of each of the component devices; The life evaluation test result of the product to be tested is determined according to the failure correlation between the component devices and the life distribution function of the component devices.

2. The method according to claim 1, characterized in that The step of obtaining test data of each of the component devices according to the failure type of each of the component devices comprises: According to the failure type of each component device, obtaining the detection data acquisition requirement of each component device; According to each of the test data acquisition requirements, in the mapping relationship between the test data acquisition requirements and the test detection methods, a test detection method matching each of the test data acquisition requirements is acquired; Each of the component devices is tested using a test detection method that matches the requirements for obtaining each of the test data to obtain test detection data of each of the component devices.

3. The method according to claim 1 or 2, characterized in that: When the component device is a hard failure device, the data type includes failure time data; and according to the data type of each test detection data, performing data analysis on each test detection data to obtain the life distribution function of each component device includes: Determine a failure likelihood function of the hard failure device according to the number of failed devices in the hard failure device, the failure time data of each of the failed devices, and the mathematical statistics model of the hard failure device; A life distribution function of the hard failure device is determined according to the failure likelihood function.

4. The method according to claim 1 or 2, characterized in that: When the component device is a first soft failure device, the data type includes first performance degradation data; and performing data analysis on each of the test detection data according to the data type of each of the test detection data to obtain the life distribution function of each of the component devices includes: Acquire, according to the first performance degradation data, a detection time series and a detection value series of a performance parameter of the first soft failure device; According to the detection time series and the detection value series, a preset neural network is trained to obtain a performance detection value prediction model; According to the performance detection value prediction model, a life distribution function of the first soft failure device is determined.

5. The method according to claim 4, characterized in that Determining the life distribution function of the first soft failure device according to the performance detection value prediction model includes: Inputting the candidate time series into the performance detection value prediction model to obtain the detection value prediction sequence output by the performance detection value prediction model; Determining the failure time of the first soft failure device according to the candidate time sequence and the detection value prediction sequence; A life distribution function of the first soft failure device is determined according to the failure time.

6. The method according to claim 5, characterized in that Determining the life distribution function of the first soft failure device according to the failure time includes: Determining a life assessment parameter of the first soft failure device according to the number of devices of the first soft failure device and the failure time; A life distribution function of the first soft failure device is determined according to the life assessment parameter.

7. The method according to claim 1 or 2, characterized in that: When the component device is a second soft failure device, the data type includes second performance degradation data; the amount of the second performance degradation data is less than the amount of the first performance degradation data; and according to the data type of each of the test detection data, performing data analysis on each of the test detection data to obtain the life distribution function of each of the component devices includes: acquiring a detection value sequence of a performance parameter of the second soft-failure device according to the second performance degradation data; determining a failure time of the second soft failure device according to a failure physical model of the second soft failure device and the detection value sequence; A life distribution function of the second soft failure device is determined according to the failure time.

8. The method according to claim 7, characterized in that Determining the life distribution function of the second soft failure device according to the failure time includes: Determining a life assessment parameter of the second soft failure device according to the number of devices of the second soft failure device and the failure time; A life distribution function of the second soft failure device is determined according to the life assessment parameter.

9. The method according to claim 1 or 2, characterized in that: Determining the life evaluation test result of the product to be tested according to the failure correlation between the component devices and the life distribution function of the component devices includes: Determining the life distribution function of the product to be tested according to the failure correlation between the component devices and the life distribution function of the component devices; The life evaluation test result of the product to be tested is determined according to the acceleration factor of the product to be tested under the accelerated stress and the life distribution function of the product to be tested.

10. A mathematical and rational fusion accelerated test and life assessment device, characterized in that: The device comprises: A data acquisition module, for acquiring test data of each component device according to a failure type of each component device when different component devices of the product to be tested are placed in different accelerated test chambers and different accelerated stresses are applied to the different accelerated test chambers respectively; A data analysis module, used to perform data analysis on each of the test data according to the data type of each of the test data, to obtain a life distribution function of each of the component devices; The result determination module is used to determine the life evaluation test result of the product to be tested according to the failure correlation between the component devices and the life distribution function of the component devices.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

Images