Method and system for variable environmental input life assessment based on 71-degree method model
By combining a specified distribution function with the 71-degree method model, and using time slicing and Arrhenius model fitting, the problem of predicting failure probability in unsteady environments using the traditional 71-degree method is solved, and accurate failure probability prediction of devices under varying environments is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHENGDU GONGYUAN TECH CO LTD
- Filing Date
- 2022-10-26
- Publication Date
- 2026-08-04
AI Technical Summary
Traditional 71-degree model is difficult to effectively predict device failure probability under unsteady conditions, especially when the environment changes over time, and it cannot accurately determine the parameters of the distribution function.
By combining a specified distribution function with the 71-degree method model, the 71-degree method is extended using the time slicing method. The relationship between the time series and the sensitivity parameter is fitted using the least squares method and the Arrhenius model to calculate the failure probability under varying environmental conditions.
It enables accurate prediction of device failure probability under varying environmental conditions, expands the scope of data application, and provides an effective prediction method for subsequent applications.
Smart Images
Figure CN115577551B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electronic digital data processing technology, and in particular to a method and system for assessing the lifetime of variable environmental inputs based on the 71-degree method model. Background Technology
[0002] The 71°C method, also known as the 71°C high-temperature storage test method, is a truncated life test method. GJB 736.8-90 clarifies the relevant standards for the 71°C test method for pyrotechnics. Since the storage of pyrotechnics under natural conditions is mainly affected by temperature and humidity, once measures are taken to prevent moisture corrosion during storage, it can be simplified to a single-factor storage problem. The 71°C high-temperature storage test method uses a modified Arrhenius equation to calculate the storage time at room temperature (21°C) from the test time at high temperature (71°C).
[0003] Although the traditional 71-degree method model can predict the lifespan of a device at normal temperatures using experimental data under steady-state conditions, it is difficult to determine the failure probability of the 71-degree method model when the input is a time series and the environment changes with the time series. Summary of the Invention
[0004] Given the above background analysis, taking the normal distribution as an example, it can predict the effective probability of a device based on the time series of an unsteady environment and a given threshold. However, when the environment changes over time, environmental conditions affect the selection of parameters in the distribution function at that moment, and the 71-degree method cannot handle unsteady environment problems. To solve the above problems, this invention proposes a variable environment input lifetime assessment method and system based on the 71-degree method model. It combines a specified distribution function with the 71-degree method model to predict the effective probability of a device. By extending the 71-degree method using a time-slicing method, it can effectively solve the above problems and be applied to prediction methods for other similar distributions and models.
[0005] The technical solution adopted in this invention is as follows:
[0006] A method for assessing lifetime under varying environmental inputs based on the 71-degree method model includes the following steps:
[0007] S1. Read training and test data: Read the mean and standard deviation of the experimental time and the corresponding sensitivity parameters of the experimental samples in the training data, and read the time series and environmental conditions in the test data;
[0008] S2. Determine the relationship between the time series and the mean and standard deviation of the sensitivity parameter: Use the least squares method and the Arrhenius model to fit and solve the relationship between the time series and the mean of the sensitivity parameter, and use piecewise interpolation to determine the relationship between the time series and the standard deviation of the sensitivity parameter;
[0009] S3. Calculate the parameters of the preset distribution function under varying environmental conditions: Based on the relationship between the time series and the mean and standard deviation of the sensitivity parameters, determine the relationship between the parameters of the preset distribution function and the test samples, and obtain the parameters of the preset distribution function under varying environmental conditions;
[0010] S4. Calculate the failure probability under varying environmental conditions: Determine the effective probability threshold based on the sensitivity parameter. When the sensitivity parameter exceeds the threshold, it is judged as a failure. Based on this, the probability density function of the preset distribution function under varying environmental conditions is integrated to determine the failure probability.
[0011] Furthermore, in step S2, the relationship between the mean and the time series is determined using the Arrhenius model:
[0012] μ=e a+b*T (1)
[0013] Where μ is the mean of the experimental sample, a and b are coefficients, and T is the time series;
[0014] Then, the least squares method is used to fit the input data lnμ with the time series T to obtain the coefficients a and b, thereby determining the relationship between the time series and the mean value of the sensitivity parameter.
[0015] Furthermore, in step S4, the formula for calculating the failure probability is as follows:
[0016]
[0017] Where f(t) is the probability density function of the preset distribution function, and threshold is the threshold value.
[0018] Furthermore, the preset distribution function includes a normal distribution function.
[0019] A variable environmental input lifetime assessment system based on the 71-degree method model includes:
[0020] The data reading module is used to read the mean and standard deviation of the experimental time and the corresponding sensitivity parameters of the experimental samples in the training data, and to read the time series and environmental conditions in the test data.
[0021] Data association module: The relationship between the time series and the mean of the sensitivity parameter is solved by fitting the least squares method and the Arrhenius model, and the relationship between the time series and the standard deviation of the sensitivity parameter is determined by piecewise interpolation;
[0022] The parameter calculation module is used to determine the relationship between the parameters of the preset distribution function and the test samples based on the relationship between the time series and the mean and standard deviation of the sensitivity parameters, and to obtain the parameters of the preset distribution function under varying environmental conditions.
[0023] The probability calculation module is used to determine the effective probability threshold based on the sensitivity parameter. When the sensitivity parameter exceeds the threshold, it is judged as a failure. Based on this, the probability density function of the preset distribution function under the changing environment is integrated to determine the failure probability.
[0024] Furthermore, in the data association module, the relationship between the mean and the time series is determined using the Arrhenius model:
[0025] μ=e a+b*T (3)
[0026] Where μ is the mean of the experimental sample, a and b are coefficients, and T is the time series;
[0027] Then, the least squares method is used to fit the input data lnμ with the time series T to obtain the coefficients a and b, thereby determining the relationship between the time series and the mean value of the sensitivity parameter.
[0028] Furthermore, in the probability calculation module, the formula for calculating the failure probability is as follows:
[0029]
[0030] Where f(t) is the probability density function of the preset distribution function, and threshold is the threshold value.
[0031] Furthermore, the preset distribution function includes a normal distribution function.
[0032] The beneficial effects of this invention are as follows: Based on the 71-degree method model under time series variable environment input, this invention can predict the failure probability of devices under a specified distribution, realize the expansion of data applications, and provide ideas for subsequent applications. Attached Figure Description
[0033] Figure 1 This is a flowchart of the variable environment input lifetime assessment method based on the 71-degree method model of the present invention.
[0034] Figure 2 This is a schematic diagram of the fitting result between the mean μ and the time series.
[0035] Figure 3 This is a schematic diagram showing the fitting results of the standard deviation σ with the time series.
[0036] Figure 4 It is a time-pass probability graph. Detailed Implementation
[0037] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments are now described. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0038] Example 1
[0039] like Figure 1 As shown, this embodiment provides a variable environment input lifetime assessment method based on the 71-degree method model. By segmenting the environmental change process, lifetime assessment is performed in each time period using the 71-degree method. Variance is calculated under different environments using experimental data, and the variance is interpolated with respect to the environment using an Arrhenius model. This interpolation serves as a parameter for the probability distribution of a specified distribution under different environments at different times. Simultaneously, the lifetime curve calculated using the segmented 71-degree method is combined with the probability distribution. Given a failure threshold, the failure probability at each time point can be assessed. The method includes the following steps:
[0040] S1. Read training and test data:
[0041] Read the mean and standard deviation of the experimental time and its corresponding sensitivity parameters for the experimental samples from the training data. Read the time series and environmental conditions from the test data. Assume that in the training data, the mean value of a set of experimental samples corresponding to the experimental time of the i-th experiment is μ. i The standard deviation is σ i .
[0042] S2. Determine the relationship between the time series and the mean and standard deviation of the sensitivity parameters:
[0043] Determining the relationship between the mean and time series using the Arrhenius model:
[0044] μ=e a+b*T (1)
[0045] Where μ is the mean of the experimental sample, a and b are coefficients, and T is the time series.
[0046] Then, the least squares method is used to fit the input data lnμ to the time series T to obtain the coefficients a and b, thereby determining the relationship between the time series and the mean value of the sensitivity parameter, such as... Figure 2 The figure shows the relationship between the mean μ obtained by fitting the input instance and the time series.
[0047] Simultaneously, piecewise interpolation is used to determine the relationship between the time series and the standard deviation of the sensitivity parameter, such as... Figure 3 The figure shows the relationship between the standard deviation σ obtained from the fitting of the input example and the time series.
[0048] S3. Calculate the parameters of the preset distribution function under varying environmental conditions:
[0049] Based on the relationship between the time series and the mean and standard deviation of the sensitivity parameters, the relationship between the parameters of the preset distribution function and the test samples is determined, thus obtaining the parameters of the preset distribution function under varying environmental conditions. Preferably, this embodiment uses the normal distribution function as the preset distribution function.
[0050] S4. Calculate the failure probability under varying environmental conditions:
[0051] The failure probability at each time point is calculated based on the fitted mean μ and standard deviation σ. Given a threshold, failure is determined when the sensitivity parameter exceeds the threshold. The distribution at each time point follows a normal distribution, and the failure probability at each time point is shown below.
[0052]
[0053] Where f(t) is the probability density function of the preset distribution function, and threshold is the threshold value.
[0054] Example 2
[0055] This embodiment provides a variable environment input lifetime assessment system based on the 71-degree method model, including a data reading module, a data association module, a parameter calculation module, and a probability calculation module, wherein:
[0056] The data reading module is used to read training and test data. Specifically, it reads the experimental time and the mean and standard deviation of the corresponding experimental sample sensitivity parameters from the training data, and reads the time series and environmental conditions from the test data. Assume that in the training data, the mean value of a set of experimental samples corresponding to the experimental time of the i-th experiment is μ. i The standard deviation is σ i .
[0057] The data association module is used to determine the relationship between the time series and the sensitivity parameters, mean and standard deviation. Specifically, it uses an Arrhenius model to determine the relationship between the mean and the time series:
[0058] μ=e a+b*T (1)
[0059] Where μ is the mean of the experimental sample, a and b are coefficients, and T is the time series.
[0060] Then, the least squares method is used to fit the input data lnμ to the time series T to obtain the coefficients a and b, thereby determining the relationship between the time series and the mean value of the sensitivity parameter, such as... Figure 2 The figure shows the relationship between the mean μ obtained by fitting the input instance and the time series.
[0061] Simultaneously, piecewise interpolation is used to determine the relationship between the time series and the standard deviation of the sensitivity parameter, such as... Figure 3 The figure shows the relationship between the standard deviation σ obtained from the fitting of the input example and the time series.
[0062] The parameter calculation module is used to calculate the parameters of a preset distribution function under varying environmental conditions. Specifically, based on the relationship between the time series and the mean and standard deviation of the sensitivity parameters, the relationship between the parameters of the preset distribution function and the test samples is determined, thus obtaining the parameters of the preset distribution function under varying environmental conditions. Preferably, this embodiment uses a normal distribution function as the preset distribution function.
[0063] The probability calculation module is used to calculate the failure probability under varying environmental conditions. Specifically, it calculates the failure probability corresponding to a given time point based on the fitted mean μ and standard deviation σ. Given a threshold, failure is determined when the sensitivity parameter exceeds the threshold. The distribution at each time point follows a normal distribution, and the failure probability at each time point is shown below.
[0064]
[0065] Where f(t) is the probability density function of the preset distribution function, and threshold is the threshold value.
[0066] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
Claims
1. A method for assessing the lifetime of a variable environment based on a 71-degree method model, characterized in that, Includes the following steps: S1. Read training and test data: Read the mean and standard deviation of the experimental time and the corresponding sensitivity parameters of the experimental samples in the training data, and read the time series and environmental conditions in the test data; S2. Determine the relationship between the time series and the mean and standard deviation of the sensitivity parameter: Use the least squares method and the Arrhenius model to fit and solve the relationship between the time series and the mean of the sensitivity parameter, and use piecewise interpolation to determine the relationship between the time series and the standard deviation of the sensitivity parameter; S3. Calculate the parameters of the preset distribution function under varying environmental conditions: Based on the relationship between the time series and the mean and standard deviation of the sensitivity parameters, determine the relationship between the parameters of the preset distribution function and the test samples, and obtain the parameters of the preset distribution function under varying environmental conditions; S4. Calculate the failure probability under varying environmental conditions: Determine the effective probability threshold based on the sensitivity parameter. When the sensitivity parameter exceeds the threshold, it is judged as a failure. Based on this, the probability density function of the preset distribution function under varying environmental conditions is integrated to determine the failure probability.
2. The method for assessing the lifetime of varying environmental inputs based on the 71-degree method model according to claim 1, characterized in that, In step S2, the relationship between the mean and the time series is determined using the Arrhenius model: μ=e a+b*T (1) Where μ is the mean of the experimental sample, a and b are coefficients, and T is the time series; Then, the least squares method is used to fit the input data lnμ with the time series T to obtain the coefficients a and b, thereby determining the relationship between the time series and the mean value of the sensitivity parameter.
3. The method for assessing the lifetime of varying environmental inputs based on the 71-degree method model according to claim 1, characterized in that, In step S4, the failure probability is calculated using the following formula: Where f(t) is the probability density function of the preset distribution function, and threshold is the threshold value.
4. The method for assessing the lifetime of varying environmental inputs based on the 71-degree method model according to any one of claims 1-3, characterized in that, The preset distribution function includes the normal distribution function.
5. A variable environmental input lifetime assessment system based on the 71-degree method model, characterized in that, include: The data reading module is used to read the mean and standard deviation of the experimental time and the corresponding sensitivity parameters of the experimental samples in the training data, and to read the time series and environmental conditions in the test data. Data association module: The relationship between the time series and the mean of the sensitivity parameter is solved by fitting the least squares method and the Arrhenius model, and the relationship between the time series and the standard deviation of the sensitivity parameter is determined by piecewise interpolation; The parameter calculation module is used to determine the relationship between the parameters of the preset distribution function and the test samples based on the relationship between the time series and the mean and standard deviation of the sensitivity parameters, and to obtain the parameters of the preset distribution function under varying environmental conditions. The probability calculation module is used to determine the effective probability threshold based on the sensitivity parameter. When the sensitivity parameter exceeds the threshold, it is judged as a failure. Based on this, the probability density function of the preset distribution function under the changing environment is integrated to determine the failure probability.
6. The variable environment input lifetime assessment system based on the 71-degree method model according to claim 5, characterized in that, In the data association module, the relationship between the mean and the time series is determined using the Arrhenius model: μ=e a+b*T (3) Where μ is the mean of the experimental sample, a and b are coefficients, and T is the time series; Then, the least squares method is used to fit the input data lnμ with the time series T to obtain the coefficients a and b, thereby determining the relationship between the time series and the mean value of the sensitivity parameter.
7. The variable environmental input lifetime assessment system based on the 71-degree method model according to claim 5, characterized in that, In the probability calculation module, the formula for calculating the failure probability is as follows: Where f(t) is the probability density function of the preset distribution function, and threshold is the threshold value.
8. The variable environmental input lifetime assessment system based on the 71-degree method model according to any one of claims 5-7, characterized in that, The preset distribution function includes the normal distribution function.