Test method and system for verifying reliability of intelligent control device

Abstract

The invention discloses a test method and system for verifying the reliability of an intelligent control device, and the method comprises the steps: selecting a plurality of sample intelligent control devices based on the reliability parameters of the intelligent control devices, and carrying out the Ville fitting of the first failure data of the sample intelligent control devices in a single physical field aging test, obtaining average fault-free time of a single physical field and screening a plurality of normal sample intelligent control devices from the plurality of sample intelligent control devices; performing Ville fitting and maximum likelihood estimation on second failure data of each normal sample intelligent control device in the multi-physics field aging test to obtain a corrected failure shape parameter and a corrected failure scale parameter; and obtaining a reliability verification result of the intelligent control device according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter and the average fault-free time of the single physical field, thereby improving the accuracy and efficiency of the reliability test.

Description

Technical Field

[0001] The present invention relates to the technical field of device testing, and in particular to a testing method and system for verifying the reliability of an intelligent control device. Background Art

[0002] With the rapid development of intelligent and digital power systems, intelligent control devices, as the core nodes of grid perception, decision-making, and execution, undertake key tasks such as data collection, information transmission, real-time calculation, precise control, and status monitoring. Therefore, the reliability of these intelligent devices determines the stable operation of the power grid.

[0003] In the prior art, when conducting reliability tests on smart devices, single physical field tests (such as temperature or vibration only) are often used to reduce testing costs. However, single physical field tests cannot reflect the actual multi-field coupling failure, resulting in low test accuracy. If simplified methods are used, it is difficult to balance test accuracy and cost. In addition, traditional multi-physical field coupling testing methods require dynamic optimization of sample size and test time, involving complex weight calculations and real-time corrections, resulting in low test efficiency. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention discloses a test method and system for verifying the reliability of an intelligent control device, which are used to improve the accuracy and efficiency of the test.

[0005] In order to achieve the above object, the present invention discloses a test method for verifying the reliability of an intelligent control device, comprising:

[0006] Selecting a plurality of sample intelligent control devices based on the reliability parameters of the intelligent control devices, and obtaining first failure data of each of the sample intelligent control devices in a single physical field aging test;

[0007] Performing Wilbull fitting on the first failure data to obtain a single physical field mean time between failures based on the first failure shape parameter and the first failure scale parameter obtained after fitting, and screening a plurality of normal sample intelligent control devices from the plurality of sample intelligent control devices;

[0008] Obtaining second failure data of each normal sample intelligent control device in the multi-physics field aging test, and performing Wilbull fitting on the second failure data to perform maximum likelihood estimation on the second failure shape parameter and the second failure scale parameter obtained after the fitting, to obtain a modified failure shape parameter and a modified failure scale parameter;

[0009] A reliability verification result of the intelligent control device is obtained according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter, and the single physical field mean time between failures.

[0010] The present invention discloses a test method for verifying the reliability of an intelligent control device. When performing a reliability test on a target type of intelligent control device, multiple sample intelligent control devices are selected for testing based on the reliability parameters of the intelligent control device, so as to ensure the accuracy of the test according to the number of sample intelligent control devices. Then, when performing the reliability test, the sample intelligent control devices participating in the test are first subjected to a single physical field aging test to obtain the single physical field mean time between failures of the intelligent control device during the single physical field test process, thereby providing a judgment basis for subsequent reliability verification. At the same time, abnormal sample intelligent control devices are screened out based on the single physical field mean time between failures to ensure the accuracy of subsequent multi-physical field testing. During the multi-physical field aging test process, the failure shape parameter and second failure scale parameter of the intelligent control device under multi-physical fields are first obtained, and then the parameters are dynamically adjusted through maximum likelihood estimation to improve the accuracy of the test. Finally, the reliability test verification results of the device are obtained using the obtained test parameters within the single physical field and the test parameters within the multi-physical field, thereby improving the efficiency and accuracy of the reliability test.

[0011] As a preferred example, the method of selecting a plurality of sample intelligent control devices based on the reliability parameters of the intelligent control device and obtaining the first failure data of each of the sample intelligent control devices in a single physical field aging test includes:

[0012] Obtaining a standard reliability and a standard confidence of the intelligent control device, and using the standard reliability and the standard confidence as reliability parameters of the intelligent control device;

[0013] Determining the number of sample intelligent control devices to be tested based on the standard reliability and standard confidence;

[0014] Based on the sample quantity, multiple sample intelligent control devices are selected, and an accelerated aging test of a single temperature field is performed on each of the sample intelligent control devices to obtain first failure data corresponding to each of the sample intelligent control devices; wherein, the first failure data includes the life span of the sample intelligent control device when performance degradation is reached and the current temperature of the single temperature field.

[0015] In the above scheme, the reliability parameters of the equipment to be tested are used to select the number of devices participating in the test, so as to reduce the cost of participating in the test and improve the efficiency of the test through the number and the reliability parameters. At the same time, multiple devices participating in the test are used to demonstrate the reliability of their corresponding types of intelligent control devices, thereby improving the accuracy of reliability test verification.

[0016] As a preferred example, performing Wilbull fitting on the first failure data to obtain a single physical field mean time between failures based on the first failure shape parameter and the first failure scale parameter obtained after fitting, and screening a number of normal sample intelligent control devices from the plurality of sample intelligent control devices, includes:

[0017] Summarizing the first failure data corresponding to each of the sample intelligent control devices to obtain a plurality of first failure data;

[0018] Fitting the plurality of first failure data according to a preset Wilbull function to obtain a first failure shape parameter and a first failure scale parameter;

[0019] Obtaining a single physical field mean time between failures of the intelligent control device in a single physical field aging test according to the first failure shape parameter and the first failure scale parameter;

[0020] Obtaining a confidence interval according to the first failure scale parameter, the first failure scale parameter, and a preset statistical method;

[0021] For any of the sample intelligent control devices;

[0022] When the life span of the sample intelligent control device is within the confidence interval, the sample intelligent control device is selected as the normal sample intelligent control device.

[0023] In this approach, a Weibull function is used to fit the failure data generated during device testing to obtain shape parameters and scale parameters that represent the device's failure mode. The shape parameter determines the failure mode's trend, while the scale parameter represents the characteristic lifespan. The single-field mean time between failures (MTBF) is then calculated based on the trend and characteristic lifespan. This not only provides a basis for subsequent device reliability testing and verification, but also uses the scale parameter to filter out abnormal samples, reducing the amount of data required for subsequent testing and improving test efficiency and accuracy.

[0024] As a preferred example, the obtaining of the second failure data of each normal sample intelligent control device in the multi-physical field aging test and performing Wilbull fitting on the second failure data includes:

[0025] Obtaining a coupling acceleration factor of a multi-physical field coupling stress according to an acceleration factor of each single physical field in a plurality of single physical fields; wherein the multi-physical field coupling stress includes temperature, humidity, and vibration stress;

[0026] Performing a multi-physical field aging test on each of the normal sample intelligent control devices according to the multi-physical field coupling stress to obtain second failure data corresponding to each of the normal sample intelligent control devices;

[0027] Summarizing the second failure data corresponding to each of the normal sample intelligent control devices to obtain a plurality of second failure data;

[0028] A plurality of the second failure data are fitted according to a preset Wilbull function to obtain a second failure shape parameter and a second failure scale parameter.

[0029] In this approach, multi-field coupled stress is generated based on the acceleration factor of a single physical field, improving the accuracy of multi-field aging testing. The same technical features are then used to obtain the failure data generated by the device during testing, and the shape and scale parameters that represent the device's failure mode are then fitted. This provides a basis for subsequent reliability testing and verification of the device, improving test accuracy.

[0030] As a preferred example, performing maximum likelihood estimation on the second failure shape parameter and the second failure scale parameter obtained after fitting to obtain a modified failure shape parameter and a modified failure scale parameter includes:

[0031] After the multi-physical field aging test, the normal sample intelligent control device is divided into a failed sample intelligent control device and a non-failed sample intelligent control device;

[0032] Obtaining the failure time of each of the failed sample intelligent control devices and the truncation time of each of the non-failed sample intelligent control devices;

[0033] The second failure shape parameter and the second failure scale parameter are dynamically modified according to the failure time, the truncation time and a preset maximum likelihood estimation function to obtain a modified failure shape parameter and a modified failure scale parameter.

[0034] In the above solution, the second failure shape parameter and the second failure scale parameter are dynamically adjusted through the maximum likelihood estimation function to improve the accuracy of the failure shape parameter and the failure scale parameter, thereby improving the accuracy of the test verification.

[0035] As a preferred example, obtaining a reliability verification result of the intelligent control device based on the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter, and the single physical field mean time between failures includes:

[0036] Obtaining a multi-physics field mean time between failures of the intelligent control device in a multi-physics field aging test according to the corrected failure shape parameter and the corrected failure scale parameter;

[0037] Obtaining a probability that the intelligent control device does not fail within a given time through a preset enhanced Boolean hybrid model based on the modified failure shape parameter, the modified failure scale parameter, the first failure shape parameter, and the first failure scale parameter;

[0038] According to the comparison result of the multi-physical field mean time between failures and the single physical field mean time between failures and the probability, a reliability verification result of the intelligent control device is obtained.

[0039] In the above scheme, an enhanced authoritative Boolean hybrid model is used to calculate the reliability of the device, and the multi-field test data is integrated to generate the mean time between failures under multi-physical fields. Then, by comparing the mean time between failures in multi-physical fields and that in single physical fields, the accuracy of the device reliability assessment is improved.

[0040] As a preferred example, the calculation expression of the sample quantity is:

[0041]

[0042] Wherein, N represents the number of samples; R represents the standard reliability; and C represents the standard confidence.

[0043] In the above solution, the reliability of the device to be tested is used as a parameter to select the number of devices to participate in the test, thereby reducing the difficulty of device selection and improving the efficiency and accuracy of the test.

[0044] As a preferred example, the calculation expression of mean time between failures is as follows:

[0045]

[0046] Among them, the MTBF represents the mean time between failures, and the mean time between failures is the single physical field mean time between failures or the multi-physical field mean time between failures; the η represents the first failure scale parameter or the modified failure scale parameter; the β represents the first failure shape parameter or the modified failure shape parameter.

[0047] In the above scheme, the mean time between failures is calculated based on the shape parameters that determine the failure mode and the scale parameters that represent the characteristic life, so as to improve the evaluation accuracy of the mean time between failures and thus improve the accuracy of test verification.

[0048] As a preferred example, the calculation expression of the Wilbull function is as follows:

[0049]

[0050] Here, t represents time.

[0051] In the above solution, the Wilbull function is used to evaluate the shape parameter and the scale parameter, so as to improve the accuracy of the reliability test verification through the shape parameter and the scale parameter.

[0052] On the other hand, the present invention discloses a test system for verifying the reliability of an intelligent control device, comprising a single physical field aging module, a device screening module, a multi-physical field aging module, and a verification module;

[0053] The single physical field aging module is used to select a plurality of sample intelligent control devices based on the reliability parameters of the intelligent control devices, and obtain the first failure data of each of the sample intelligent control devices in the single physical field aging test;

[0054] The device screening module is used to perform Wilbull fitting on the first failure data to obtain a single physical field mean time between failures based on the first failure shape parameter and the first failure scale parameter obtained after fitting, and to screen a number of normal sample intelligent control devices from the plurality of sample intelligent control devices;

[0055] The multi-physics field aging module is used to obtain the second failure data of each normal sample intelligent control device in the multi-physics field aging test, and perform Wilbull fitting on the second failure data to perform maximum likelihood estimation on the second failure shape parameter and the second failure scale parameter obtained after fitting, so as to obtain a modified failure shape parameter and a modified failure scale parameter;

[0056] The verification module is used to obtain a reliability verification result of the intelligent control device according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter, and the single physical field mean time between failures.

[0057] The present invention discloses a test system for verifying the reliability of intelligent control devices. When performing reliability testing on a target type of intelligent control device, multiple sample intelligent control devices are selected for testing based on the reliability parameters of the intelligent control device, so as to ensure the accuracy of the test according to the number of sample intelligent control devices. Then, when performing the reliability test, the sample intelligent control devices participating in the test are first subjected to a single physical field aging test to obtain the single physical field mean time between failures of the intelligent control device during the single physical field test process, thereby providing a judgment basis for subsequent reliability verification. At the same time, abnormal sample intelligent control devices are screened out based on the single physical field mean time between failures to ensure the accuracy of subsequent multi-physical field testing. During the multi-physical field aging test process, the failure shape parameter and second failure scale parameter of the intelligent control device under multi-physical fields are first obtained, and then the parameters are dynamically adjusted through maximum likelihood estimation to improve the accuracy of the test. Finally, the reliability test verification results of the device are obtained using the obtained test parameters within the single physical field and the test parameters within the multi-physical field, thereby improving the efficiency and accuracy of the reliability test. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0059] Figure 1 1 is a flow chart of a test method for verifying the reliability of an intelligent control device provided by an embodiment of the present invention;

[0060] Figure 2 1 is a schematic structural diagram of a test system for verifying the reliability of an intelligent control device provided by an embodiment of the present invention;

[0061] Figure 3 1 is a flow chart of a test method for verifying the reliability of an intelligent control device provided by another embodiment of the present invention;

[0062] Figure 4 1 is a schematic diagram of failure time distribution of a single physical field provided by another embodiment of the present invention;

[0063] Figure 5 This is a schematic diagram of multi-physics field coupling failure time distribution provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0067] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0068] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0069] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0070] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0071] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0072] See also Figure 1 To solve the problems of low testing efficiency and low testing accuracy in the prior art, an embodiment of the present invention provides a testing method for verifying the reliability of an intelligent control device, comprising:

[0073] Step 101: Select multiple sample intelligent control devices based on the reliability parameters of the intelligent control devices, and obtain the first failure data of each of the sample intelligent control devices in a single physical field aging test.

[0074] In this embodiment, this step mainly includes: obtaining the standard reliability and standard confidence of the intelligent control device, and using the standard reliability and standard confidence as the reliability parameters of the intelligent control device; determining the sample number of sample intelligent control devices participating in the test based on the standard reliability and standard confidence; selecting multiple sample intelligent control devices based on the sample number, and performing an accelerated aging test of a single temperature field on each of the sample intelligent control devices to obtain the first failure data corresponding to each of the sample intelligent control devices; wherein, the first failure data includes the life span of the sample intelligent control device when it reaches performance degradation and the current temperature of the single temperature field.

[0075] The calculation expression of the sample quantity is:

[0076]

[0077] Wherein, N represents the number of samples; R represents the standard reliability; and C represents the standard confidence.

[0078] In this embodiment, this step utilizes the reliability parameters of the equipment to be tested to select the number of devices participating in the test, so as to reduce the cost of participating in the test and improve the efficiency of the test through the number and the reliability parameters. At the same time, the multiple devices participating in the test are used to demonstrate the reliability of the corresponding type of intelligent control device, thereby improving the accuracy of reliability test verification.

[0079] Step 102: Perform Wilbull fitting on the first failure data to obtain a single physical field mean time between failures based on the first failure shape parameter and the first failure scale parameter obtained after fitting, and select a number of normal sample intelligent control devices from the plurality of sample intelligent control devices.

[0080] In this embodiment, this step mainly includes: summarizing the first failure data corresponding to each of the sample intelligent control devices to obtain multiple first failure data; fitting the multiple first failure data according to a preset Wilbull function to obtain a first failure shape parameter and a first failure scale parameter; obtaining the single physical field mean time between failures of the intelligent control device in the single physical field aging test according to the first failure shape parameter and the first failure scale parameter; obtaining a confidence interval according to the first failure scale parameter, the first failure scale parameter and a preset statistical method; for any of the sample intelligent control devices; when the life span of the sample intelligent control device is within the confidence interval, the sample intelligent control device is selected as the normal sample intelligent control device.

[0081] The calculation expression of the mean time between failures is as follows:

[0082]

[0083] Among them, the MTBF represents the mean time between failures, and the mean time between failures is the single physical field mean time between failures or the multi-physical field mean time between failures; the η represents the first failure scale parameter or the modified failure scale parameter; the β represents the first failure shape parameter or the modified failure shape parameter.

[0084] The calculation expression of the Wilbur function is as follows:

[0085]

[0086] Here, t represents time.

[0087] In this embodiment, this step uses a Weibull function to fit the failure data generated during device testing to obtain shape parameters and scale parameters that represent the device's failure mode. The shape parameter determines the failure mode's trend, while the scale parameter represents the characteristic lifespan. Based on this trend and characteristic lifespan, the single-field mean time between failures (MTBF) is calculated. This not only provides a basis for subsequent device reliability testing and verification, but also uses the scale parameter to filter out abnormal samples, reducing the amount of data required for subsequent testing and improving test efficiency and accuracy.

[0088] Step 103: Obtaining the second failure data of each normal sample intelligent control device in the multi-physics field aging test, and performing Wilbull fitting on the second failure data to perform maximum likelihood estimation on the second failure shape parameter and the second failure scale parameter obtained after fitting, to obtain a modified failure shape parameter and a modified failure scale parameter;

[0089] In this embodiment, this step mainly includes: first, according to the acceleration factor of each single physical field in multiple single physical fields, the coupling acceleration factor of the multi-physical field coupling stress is obtained; wherein, the multi-physical field coupling stress includes temperature, humidity and vibration stress; according to the multi-physical field coupling stress, a multi-physical field aging test is performed on each of the normal sample intelligent control devices to obtain the second failure data corresponding to each of the normal sample intelligent control devices; the second failure data corresponding to each of the normal sample intelligent control devices are summarized to obtain multiple second failure data; the multiple second failure data are fitted according to the preset Wilbull function to obtain the second failure shape parameter and the second failure scale parameter.

[0090] Secondly, after the multi-physical field aging test, the normal sample intelligent control device is divided into failed sample intelligent control devices and non-failed sample intelligent control devices; the failure time of each failed sample intelligent control device and the truncation time of each non-failed sample intelligent control device are obtained; the second failure shape parameter and the second failure scale parameter are dynamically corrected according to the failure time, the truncation time and the preset maximum likelihood estimation function to obtain the corrected failure shape parameter and the corrected failure scale parameter.

[0091] In this embodiment, this step first generates multi-physics field coupling stress based on the acceleration factor of a single physical field to improve the accuracy of the multi-physics field aging test. Then, the failure data generated by the device during the test is fitted using the same technical features to obtain the shape parameters and scale parameters that demonstrate the failure mode of the device. This provides a basis for subsequent reliability testing and verification of the device, thereby improving the accuracy of the test. Secondly, the second failure shape parameter and the second failure scale parameter are dynamically adjusted through the maximum likelihood estimation function to improve the accuracy of the failure shape parameter and the failure scale parameter, thereby improving the accuracy of the test verification.

[0092] Step 104: Obtain a reliability verification result of the intelligent control device according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter, and the single physical field mean time between failures.

[0093] In this embodiment, this step mainly includes: obtaining the multi-physical field mean time between failures of the intelligent control device in the multi-physical field aging test according to the corrected failure shape parameter and the corrected failure scale parameter; obtaining the probability that the intelligent control device will not fail within a given time through a preset enhanced Boolean hybrid model according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter and the first failure scale parameter; obtaining the reliability verification result of the intelligent control device according to the comparison result of the multi-physical field mean time between failures and the single physical field mean time between failures and the probability.

[0094] In this embodiment, this step uses an enhanced authoritative Boolean hybrid model to calculate the reliability of the device, and integrates multi-field test data to generate the mean time between failures under multi-physical fields, and then improves the accuracy of the device reliability assessment by comparing the mean time between failures in multi-physical fields and that in a single physical field.

[0095] On the other hand, this embodiment also discloses a test system for verifying the reliability of the intelligent control device. For the specific structure of the test system, please refer to Figure 2 , including a single physical field aging module 201, a device screening module 202, a multi-physical field aging module 203 and a verification module 204.

[0096] The single physical field aging module 201 is used to select multiple sample intelligent control devices based on the reliability parameters of the intelligent control devices, and obtain the first failure data of each of the sample intelligent control devices in the single physical field aging test.

[0097] The device screening module 202 is used to perform Wilbull fitting on the first failure data to obtain a single physical field mean time between failures based on the first failure shape parameter and the first failure scale parameter obtained after fitting, and to screen a number of normal sample intelligent control devices from the plurality of sample intelligent control devices.

[0098] The multi-physics field aging module 203 is used to obtain the second failure data of each normal sample intelligent control device in the multi-physics field aging test, and perform Wilbull fitting on the second failure data to perform maximum likelihood estimation on the second failure shape parameter and the second failure scale parameter obtained after fitting, so as to obtain the corrected failure shape parameter and the corrected failure scale parameter.

[0099] The verification module 204 is configured to obtain a reliability verification result of the intelligent control device according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter, and the single physical field mean time between failures.

[0100] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention, which can implement any one of the above-mentioned method embodiments of the present invention to provide a test method for verifying the reliability of an intelligent control device.

[0101] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. Furthermore, in the drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which may be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement the present invention without inventive effort.

[0102] Based on the above-mentioned embodiment of the test method for verifying the reliability of an intelligent control device, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the test method for verifying the reliability of an intelligent control device according to any embodiment of the present invention.

[0103] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0104] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0105] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0106] Based on the above-mentioned method embodiments, another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the test method for verifying the reliability of the intelligent control device described in any one of the above-mentioned method embodiments of the present invention.

[0107] Wherein, the module / unit integrated in the device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry 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.

[0108] This embodiment provides a test method, system, device, and medium for verifying the reliability of an intelligent control device. When performing a reliability test on a target type of intelligent control device, multiple sample intelligent control devices are selected for testing based on the reliability parameters of the intelligent control device, so as to ensure the accuracy of the test according to the number of sample intelligent control devices. Then, when performing the reliability test, the sample intelligent control devices participating in the test are first subjected to a single physical field aging test to obtain the single physical field mean time between failures of the intelligent control device during the single physical field test process, thereby providing a basis for subsequent reliability verification. At the same time, abnormal sample intelligent control devices are screened out based on the single physical field mean time between failures to ensure the accuracy of subsequent multi-physical field testing. During the multi-physical field aging test process, the failure shape parameter and the second failure scale parameter of the intelligent control device under multi-physical fields are first obtained, and then the parameters are dynamically adjusted through maximum likelihood estimation to improve the accuracy of the test. Finally, the reliability test verification results of the device are obtained using the obtained test parameters in the single physical field and the test parameters in the multi-physical fields, thereby improving the efficiency and accuracy of the reliability test.

[0109] Example 2

[0110] In the existing technology for verifying the reliability of intelligent control devices, a single physical field is often used for reliability testing. In addition, in the equipment identification test standards, only some test items and their test requirements are listed. In addition, the relevant test requirements for equipment identification only mechanically list the test items, and no complete and clear test requirements and test methods are formed for equipment identification testing, resulting in low quality reliability of equipment identification.

[0111] To solve the above technical problems, refer to Figure 3 This embodiment takes a highly reliable, full-stack, domestically produced new energy intelligent control device with built-in safety functions as an example and provides a test method for verifying the reliability of the intelligent control device to verify the reliability of the new energy intelligent control device. The method includes:

[0112] Step 301: Based on the reliability parameters of the target test intelligent control device, several sample intelligent control devices participating in the life test are determined.

[0113] In this embodiment, the initial number of samples for the target test intelligent control device in the life test is determined based on the reliability parameters of the target test intelligent control device. The reliability parameters include the standard confidence and standard reliability of the target test intelligent control device, and the parameters of the standard confidence and standard reliability are pre-set based on the performance of the device.

[0114] In one embodiment of this embodiment, based on the fields of power equipment and industrial control, a standard reliability of 95% and a standard confidence level of 90% are common reliability verification benchmarks, taking into account both technical feasibility and economic efficiency. Therefore, the reliability R of the target test intelligent control device is set to 95%, the confidence level C is set to 90%, and the allowed failure number k is set to 0. In this embodiment, the number of sample intelligent control devices is calculated as follows:

[0115]

[0116] Wherein, N represents the number of samples; R represents the standard reliability; and C represents the standard confidence.

[0117] Substituting R=95% and confidence level C=90% into the above data calculation formula, the solution yields N, i.e., the number of sample intelligent control devices, as 45.

[0118] Step 302: Perform a single temperature field accelerated aging test on each of the sample intelligent control devices to obtain first failure data corresponding to each of the sample intelligent control devices, and use a Weibull function to fit multiple failure data corresponding to multiple sample intelligent control devices to obtain a first shape parameter and a first scale parameter to calculate a single-field mean time between failures.

[0119] In this embodiment, an accelerated aging test is performed on each of the sample intelligent control devices according to a preset single temperature field, and the number of failure samples of the single temperature field is set to 0 in advance. The constraint condition is set that the sample intelligent control device will not fail during the aging test of the single temperature field.

[0120] Then, during the aging test, the life span of each sample intelligent control device and the temperature of the temperature field in which it is located when performance degradation is about to occur are collected, and the temperature and the life span are used as failure data of the sample intelligent control device.

[0121] Secondly, the failure data of the plurality of sample intelligent control devices are aggregated to fit the failure data using a preset Wilbull function to obtain a first shape parameter and a first scale parameter. The calculation expression of the Wilbull function is:

[0122]

[0123] Wherein, η represents the first failure scale parameter; β represents the first failure shape parameter or the modified failure shape parameter; and t represents time.

[0124] Furthermore, the single-field mean time between failures of the target test intelligent control device under a single temperature field is calculated based on the first shape parameter and the first scale parameter to obtain a first test result. The calculation expression of the single-field mean time between failures is as follows:

[0125]

[0126] Here, the MTBF stands for mean time between failures.

[0127] In one embodiment of this embodiment, refer to Figure 4 From the single physical field failure time distribution diagram provided for this embodiment, it can be seen that when the Weibull function output parameters are: β = 1.5, η = 380 hours, the single field mean time between failures = 380 × Γ (1.556) ≈ 380 × 0.903 = 343 hours.

[0128] Step 303: Filter out a number of normal sample intelligent control devices from the plurality of sample intelligent control devices according to the first scale parameter, the first shape parameter and the failure data.

[0129] In this embodiment, a confidence interval is obtained using a preset statistical method based on the first scale parameter and the first shape parameter. A determination is made as to whether each sample intelligent control device is an abnormal sample intelligent control device based on the lifespan in the failure data of the sample intelligent control device and the confidence interval. If the sample intelligent control device is determined to be an abnormal sample intelligent control device, the device is eliminated.

[0130] In one implementation of this embodiment, taking the temperature field as an example, when the Weibull function outputs parameters: β = 1.5, η = 380 hours, according to β = 1.5, η = 380, the confidence interval obtained by the preset statistical method is [310, 460]. At this time, samples whose failure time exceeds the confidence interval are regarded as abnormal. For example, if the failure time of a sample intelligent control device is 200 hours, it is determined to be an abnormal sample and is eliminated.

[0131] Step 304: Apply a multi-physics coupling stress with a preset multi-physics acceleration factor to each of the normal sample intelligent control devices to obtain second failure data corresponding to each normal sample intelligent control device, and use the Weibull function to fit multiple second failure data corresponding to multiple sample intelligent control devices to obtain second shape parameters and second scale parameters.

[0132] In this embodiment, the acceleration factor of the multi-physical field is first obtained based on the acceleration factor of each single physical field. Wherein, the single physical fields are temperature, humidity and vibration stress, and the expression of the acceleration factor of the multi-physical field is:

[0133] AK 总 =AK 温度 AK 湿度 AK 振动

[0134] Among them, the AK 总 represents the acceleration factor of the multi-physics field; the AK 温度 Represents the acceleration factor of a single temperature field; the AK 湿度 Represents the acceleration factor of a single humidity field; the AK 振动 Represents the acceleration factor of a single vibration stress field.

[0135] During the multi-physical field aging test, the life span of each normal sample intelligent control device and the temperature of the temperature field in which it is located are collected when its performance is about to degrade, and the temperature and the life span are used as failure data of the normal sample intelligent control device.

[0136] Secondly, failure data of a plurality of sample intelligent control devices are aggregated to fit the failure data using a preset Wilbull function to obtain a second shape parameter and a second scale parameter.

[0137] Step 305: Dynamically modify the second shape parameter and the second scale parameter by using a maximum likelihood estimation function to generate a multi-field mean time between failures using an enhanced authoritative Boolean mixture model.

[0138] In this embodiment, this step first divides the normal sample intelligent control device into failed samples and non-failed samples based on the normal sample intelligent control device during the multi-physics field coupled stress aging test. Then, the failure time of each failed sample and the truncation time of each non-failed sample are collected. The truncation time is the last observation time of the non-failed sample in the experiment.

[0139] According to the failure time, the truncation time, the second shape parameter and the second scale parameter, a modified shape parameter and a modified scale parameter are obtained by using a maximum likelihood estimation function. The calculation expression of the maximum likelihood estimation function is:

[0140]

[0141] Among them, the β mulit represents the modified shape parameter, the η mulit represents the modified scale parameter, β1 represents the second shape parameter; η1 represents the second scale parameter; t i is the failure time of the i-th failure sample (i=1,2,…,n); the t jis the truncation time of the jth non-failed sample (j = 1, 2, …, m).

[0142] In this embodiment, it is assumed that 5 samples fail. Figure 5 A multi-physics field coupling failure time distribution diagram is provided. It can be seen that when the Weibull parameter is dynamically corrected to obtain β mulit =2.1,η mulit =820 hours, the corresponding confidence interval is [552,1147].

[0143] The multi-field test data is integrated using an augmented authoritative Boolean hybrid model based on the modified scale parameter and the first scale parameter to generate the final multi-field mean time between failures. The calculation expression of the augmented authoritative Boolean hybrid model is:

[0144]

[0145] The calculation expression for calculating the multi-field mean time between failures is as follows:

[0146]

[0147] Among them, the MTBF 多场 Represents the mean time between failures.

[0148] Step 306: Obtain a verification result of the reliability test of the target test intelligent control device according to the single-field mean time between failures and the multi-field mean time between failures.

[0149] In this embodiment, by comparing the single-field MTBF and the multi-field MTBF, it can be seen that the multi-field coupling MTBF is significantly higher than the single-field test result, indicating that the coupling effect accelerates failure and the MTBF prediction error is reduced, verifying the effectiveness of the method.

[0150] A test method for verifying the reliability of an intelligent control device provided in this embodiment is intended to solve the problem in the prior art that a single physical field test cannot reflect the actual multi-field coupling failure and that the test accuracy and cost are difficult to balance. This method uses a phased testing strategy, combines a single physical field accelerated aging test with a multi-physical field coupling stress test, and uses the Weibull distribution model to dynamically correct the shape parameter (β) and scale parameter (η) to generate the final mean time between failures (MTBF). The specific steps include: determining the initial number of samples based on the reliability parameters, conducting a single physical field test and eliminating abnormal samples; applying multi-physical field coupling stress to the remaining samples, defining an acceleration factor and correcting the Weibull model parameters; and using an enhanced Weibull hybrid model to synthesize multi-field test data to generate the final MTBF. By comparing the MTBF results of multi-physical fields and single physical fields, the effectiveness of the method was verified, and improvement suggestions were put forward, which significantly improved the prediction accuracy and the accuracy of equipment reliability assessment.

[0151] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A test method for verifying the reliability of an intelligent control device, characterized in that: include: Selecting a plurality of sample intelligent control devices based on the reliability parameters of the intelligent control devices, and obtaining first failure data of each of the sample intelligent control devices in a single physical field aging test; Performing Wilbull fitting on the first failure data to obtain a single physical field mean time between failures based on the first failure shape parameter and the first failure scale parameter obtained after fitting, and screening a plurality of normal sample intelligent control devices from the plurality of sample intelligent control devices; Obtaining second failure data of each normal sample intelligent control device in the multi-physics field aging test, and performing Wilbull fitting on the second failure data to perform maximum likelihood estimation on the second failure shape parameter and the second failure scale parameter obtained after the fitting, to obtain a modified failure shape parameter and a modified failure scale parameter; A reliability verification result of the intelligent control device is obtained according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter, and the single physical field mean time between failures.

2. A test method for verifying the reliability of an intelligent control device according to claim 1, characterized in that: The method of selecting a plurality of sample intelligent control devices based on the reliability parameters of the intelligent control device and obtaining first failure data of each of the sample intelligent control devices in a single physical field aging test includes: Obtaining a standard reliability and a standard confidence of the intelligent control device, and using the standard reliability and the standard confidence as reliability parameters of the intelligent control device; Determining the number of sample intelligent control devices to be tested based on the standard reliability and standard confidence; Based on the sample quantity, multiple sample intelligent control devices are selected, and an accelerated aging test of a single temperature field is performed on each of the sample intelligent control devices to obtain first failure data corresponding to each of the sample intelligent control devices; wherein, the first failure data includes the life span of the sample intelligent control device when performance degradation is reached and the current temperature of the single temperature field.

3. A test method for verifying the reliability of an intelligent control device according to claim 2, characterized in that: The Wilbull fitting is performed on the first failure data to obtain a single physical field mean time between failures based on the first failure shape parameter and the first failure scale parameter obtained after the fitting, and a plurality of normal sample intelligent control devices are selected from the plurality of sample intelligent control devices, including: Summarizing the first failure data corresponding to each of the sample intelligent control devices to obtain a plurality of first failure data; Fitting the plurality of first failure data according to a preset Wilbull function to obtain a first failure shape parameter and a first failure scale parameter; Obtaining a single physical field mean time between failures of the intelligent control device in a single physical field aging test according to the first failure shape parameter and the first failure scale parameter; Obtaining a confidence interval according to the first failure scale parameter, the first failure scale parameter, and a preset statistical method; For any of the sample intelligent control devices; When the life span of the sample intelligent control device is within the confidence interval, the sample intelligent control device is selected as the normal sample intelligent control device.

4. A test method for verifying the reliability of an intelligent control device according to claim 1, characterized in that: The obtaining of the second failure data of each normal sample intelligent control device in the multi-physical field aging test and performing Wilbull fitting on the second failure data includes: Obtaining a coupling acceleration factor of a multi-physical field coupling stress according to an acceleration factor of each single physical field in a plurality of single physical fields; wherein the multi-physical field coupling stress includes temperature, humidity, and vibration stress; Performing a multi-physical field aging test on each of the normal sample intelligent control devices according to the multi-physical field coupling stress to obtain second failure data corresponding to each of the normal sample intelligent control devices; Summarizing the second failure data corresponding to each of the normal sample intelligent control devices to obtain a plurality of second failure data; A plurality of the second failure data are fitted according to a preset Wilbull function to obtain a second failure shape parameter and a second failure scale parameter.

5. A test method for verifying the reliability of an intelligent control device according to claim 4, characterized in that: The performing maximum likelihood estimation on the second failure shape parameter and the second failure scale parameter obtained after fitting to obtain a modified failure shape parameter and a modified failure scale parameter includes: After the multi-physical field aging test, the normal sample intelligent control device is divided into a failed sample intelligent control device and a non-failed sample intelligent control device; Obtaining the failure time of each of the failed sample intelligent control devices and the truncation time of each of the non-failed sample intelligent control devices; The second failure shape parameter and the second failure scale parameter are dynamically modified according to the failure time, the truncation time and a preset maximum likelihood estimation function to obtain a modified failure shape parameter and a modified failure scale parameter.

6. A test method for verifying the reliability of an intelligent control device according to claim 5, characterized in that: The reliability verification result of the intelligent control device is obtained according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter, and the single physical field mean time between failures, including: Obtaining a multi-physics field mean time between failures of the intelligent control device in a multi-physics field aging test according to the corrected failure shape parameter and the corrected failure scale parameter; Obtaining a probability that the intelligent control device will not fail within a given time through a preset enhanced quorum-Boolean hybrid model based on the modified failure shape parameter, the modified failure scale parameter, the first failure shape parameter, and the first failure scale parameter; According to the comparison result of the multi-physical field mean time between failures and the single physical field mean time between failures and the probability, a reliability verification result of the intelligent control device is obtained.

7. A test method for verifying the reliability of an intelligent control device according to claim 2, characterized in that: The calculation expression of the sample quantity is: Wherein, N represents the number of samples; R represents the standard reliability; and C represents the standard confidence.

8. A test method for verifying the reliability of an intelligent control device according to any one of claim 3 or claim 6, characterized in that: The calculation expression of mean time between failures is as follows: Among them, the MTBF represents the mean time between failures, and the mean time between failures is the single physical field mean time between failures or the multi-physical field mean time between failures; the η represents the first failure scale parameter or the modified failure scale parameter; the β represents the first failure shape parameter or the modified failure shape parameter.

9. A test method for verifying the reliability of an intelligent control device according to any one of claim 3 or claim 4, characterized in that: The calculation expression of the Wilbull function is as follows: Here, t represents time.

10. A test system for verifying the reliability of an intelligent control device, characterized in that: Includes single physical field aging module, device screening module, multi-physical field aging module and verification module; The single physical field aging module is used to select a plurality of sample intelligent control devices based on the reliability parameters of the intelligent control devices, and obtain the first failure data of each of the sample intelligent control devices in the single physical field aging test; The device screening module is used to perform Wilbull fitting on the first failure data to obtain a single physical field mean time between failures based on the first failure shape parameter and the first failure scale parameter obtained after fitting, and to screen a number of normal sample intelligent control devices from the plurality of sample intelligent control devices; The multi-physics field aging module is used to obtain the second failure data of each normal sample intelligent control device in the multi-physics field aging test, and perform Wilbull fitting on the second failure data to perform maximum likelihood estimation on the second failure shape parameter and the second failure scale parameter obtained after fitting, so as to obtain a modified failure shape parameter and a modified failure scale parameter; The verification module is used to obtain a reliability verification result of the intelligent control device according to the corrected failure shape parameter, the corrected failure scale parameter, the first failure shape parameter, the first failure scale parameter, and the single physical field mean time between failures.

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