CN115577551A - Variable environment input life evaluation method and system based on 71-degree method model - Google Patents
Variable environment input life evaluation method and system based on 71-degree method model Download PDFInfo
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Abstract
The invention discloses a variable environment input life evaluation method and system based on a 71-degree method model, wherein the method comprises the following steps: reading training data and test data; determining the relation between the time series and the mean value and standard deviation of the sensitivity parameters: fitting and solving the relation between the time sequence and the mean value of the sensitivity parameters in the training data by using a least square method and an Arrhenius model, and determining the relation between the time sequence and the standard deviation of the sensitivity parameters by using segmented interpolation; calculating parameters of a preset distribution function under the variable environment condition: determining the relationship between the parameters of the preset distribution function and the test sample to obtain the parameters of the preset distribution function under the variable environment condition; calculating the failure probability under variable environmental conditions: and determining an effective probability threshold, judging that the sensitivity parameter is invalid when the sensitivity parameter exceeds the threshold, and integrating the probability density function of the preset distribution function under the variable environment condition to further determine the probability of the failure. The invention can realize the prediction of the failure probability of the device under the designated distribution.
Description
Technical Field
The invention relates to the technical field of electric digital data processing, in particular to a variable environment input life evaluation method and system based on a 71-degree model.
Background
The 71-degree method, namely the 71-DEG C high-temperature storage test method, is a tail-ending service life test method. GJB 736.8-90 defines the relevant standard of initiating explosive device test method-71 deg.C test method, and because the initiating explosive device is mainly influenced by temperature and humidity during storage under natural conditions, when measures for preventing water erosion are taken during storage, the storage problem can be simplified into that of single factor. The 7l high temperature storage test method uses a modified Arrhenius equation to calculate the storage time at normal temperature (21 ℃) from the test time at high temperature (71 ℃).
Although the conventional 71-degree method model can predict the service life of the device at normal temperature through test data in a steady environment, under the condition that the input is a time series, and the environment changes along with the time series, the failure probability of the 71-degree method model is difficult to determine.
Disclosure of Invention
In view of the above background analysis, a normal distribution is taken as an example, which can predict the effective probability of the device according to the time series of the unsteady environment and the given threshold, but when the environment changes along with the time series, the environmental condition may affect the selection of the parameters in the distribution function at this moment, and the 71-degree method cannot handle the unsteady environment problem. In order to solve the problems, the invention provides a variable environment input life evaluation method and system based on a 71-degree method model, a specified distribution function and the 71-degree method model are combined to be used for predicting the effective probability of a device, the 71-degree method is expanded by using a time slicing method, and the problems can be effectively solved and used for prediction methods of other similar distributions and models.
The technical scheme adopted by the invention is as follows:
a variable environment input life evaluation method based on a 71-degree method model comprises the following steps:
s1, reading training data and test data: reading the mean value and the standard deviation of the experiment time in the training data and the sensitivity parameters of the corresponding experiment samples, and reading the time sequence and the environmental conditions in the test data;
s2, determining the relation between the time sequence and the mean value and standard deviation of the sensitivity parameters: fitting and solving the relation between the time sequence and the mean value of the sensitivity parameters by using a least square method and an Arrhenius model, and determining the relation between the time sequence and the standard deviation of the sensitivity parameters by using segmented interpolation;
s3, calculating parameters of a preset distribution function under the variable environment condition: determining the relation between the parameters of the preset distribution function and the test sample based on the relation between the time sequence and the mean value and standard deviation of the sensitivity parameters to obtain the parameters of the preset distribution function under the variable environment condition;
s4, calculating the failure probability under the variable environmental condition: and determining an effective probability threshold according to the sensitivity parameter, judging that the sensitivity parameter is invalid when the sensitivity parameter exceeds the threshold, and integrating the probability density function of the preset distribution function under the variable environment condition according to the judgment to further determine the probability of the invalidation.
Further, in step S2, the relationship between the mean value and the time series is judged by the Arrhenius model:
μ＝e a+b*T (1)
wherein mu is a mean value corresponding to the test sample, a and b are coefficients, and T is a time sequence;
and fitting the input data ln mu and the time sequence T by using a least square method to obtain coefficients a and b, thereby determining the relation between the time sequence and the mean value of the sensitivity parameters.
Further, in step S4, the failure probability calculation formula is as follows:
wherein f (t) is a probability density function of a preset distribution function, and threshold is a threshold.
Further, the preset distribution function includes a normal distribution function.
A variable environment input life evaluation system based on a 71-degree method model comprises the following steps:
the data reading module is used for reading the mean value and the standard deviation of the experiment time in the training data and the sensitivity parameters of the corresponding experiment samples, and reading the time sequence and the environmental conditions in the test data;
a data association module: fitting and solving the relation between the time sequence and the mean value of the sensitivity parameters by using a least square method and an Arrhenius model, and determining the relation between the time sequence and the standard deviation of the sensitivity parameters by using segmented interpolation;
the parameter calculation module is used for determining the relationship between the parameters of the preset distribution function and the test sample based on the relationship between the time sequence and the mean value and the standard deviation of the sensitivity parameters to obtain the parameters of the preset distribution function under the variable environment condition;
and the probability calculation module is used for determining an effective probability threshold value according to the sensitivity parameter, judging that the sensitivity parameter fails when the sensitivity parameter exceeds the threshold value, integrating the probability density function of the preset distribution function under the variable environment condition according to the judgment, and further determining the failure probability.
Further, in the data association module, the relation between the mean value and the time series is judged through an Arrhenius model:
μ＝e a+b*T (3)
wherein mu is a mean value corresponding to the test sample, a and b are coefficients, and T is a time sequence;
and fitting the input data ln mu and the time sequence T by using a least square method to obtain coefficients a and b, thereby determining the relation between the time sequence and the average value of the sensitivity parameters.
Further, in the probability calculation module, the failure probability calculation formula is as follows:
wherein f (t) is a probability density function of a preset distribution function, and threshold is a threshold.
Further, the preset distribution function includes a normal distribution function.
The invention has the beneficial effects that: the method is based on a 71-degree method model under time series variable environment input, can realize the prediction of failure probability of the device under designated distribution, can realize data application expansion, and provides an idea for subsequent application.
Drawings
FIG. 1 is a flow chart of a variable environment input life evaluation method based on a 71-degree method model.
FIG. 2 is a graph showing the results of the mean μ versus time series fit.
FIG. 3 is a graph showing the fitting result of standard deviation σ to a time series.
FIG. 4 is a time-to-qualification probability graph.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, specific embodiments of the present invention will now be described. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration only, not by way of limitation, i.e., the embodiments described are intended as a selection of the best mode contemplated for carrying out the invention, not as a full mode. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a variable environment input lifetime assessment method based on a 71-degree method model, by segmenting an environment change process, performing lifetime assessment by a 71-degree method in each time period, performing variance calculation under different environments by experimental data under different environments, performing environment-related interpolation on variances by an Arrhenius model, as parameters of probability distributions of specified distributions under corresponding environments at different times, and combining a lifetime curve calculated by the segmented 71-degree method with the probability distributions, the failure probability at each time can be assessed given a failure threshold, including the following steps:
s1, reading training data and test data:
and reading the mean value and standard deviation of the experimental time in the training data and the sensitivity parameters of the corresponding experimental samples, and reading the time sequence and the environmental conditions in the test data. Assuming that the mean value corresponding to a group of experimental samples obtained at the experimental time of the ith experiment in the training data is mu i Standard deviation of σ i 。
S2, determining the relation between the time sequence and the mean value and standard deviation of the sensitivity parameters:
judging the relation between the mean value and the time sequence through an Arrhenius model:
μ＝e a+b*T (1)
wherein mu is the mean value corresponding to the test sample, a and b are coefficients, and T is a time sequence.
And fitting the input data ln mu and the time sequence T by using a least square method to obtain coefficients a and b, so as to determine the relation between the time sequence and the mean value of the sensitivity parameter, as shown in fig. 2, the relation between the mean value mu obtained by fitting under the input example and the time sequence.
Meanwhile, the relation between the time series and the standard deviation of the sensitivity parameter is determined by utilizing segmented interpolation, and as shown in fig. 3, the relation between the standard deviation sigma obtained by fitting under the input example and the time series is determined.
S3, calculating parameters of a preset distribution function under the variable environment condition:
and determining the relation between the parameters of the preset distribution function and the test sample based on the relation between the time sequence and the mean value and standard deviation of the sensitivity parameters to obtain the parameters of the preset distribution function under the variable environment condition. Preferably, the normal distribution function is used as the preset distribution function in the present embodiment.
S4, calculating the failure probability under the variable environment condition:
and calculating the failure probability corresponding to the time point according to the fitted mean value mu and the standard deviation sigma. Giving a threshold value, judging that the sensitivity parameter fails when the sensitivity parameter exceeds the threshold value, wherein the distribution of each time point is normal distribution, and the failure probability of each time point is shown as follows
Wherein f (t) is a probability density function of a preset distribution function, and threshold is a threshold.
Example 2
The embodiment provides a variable environment input life evaluation system based on a 71-degree method model, which comprises a data reading module, a data association module, a parameter calculation module and a probability calculation module, wherein:
the data reading module is used for reading the training data and the test data. Specifically, the method is used for reading the mean value and the standard deviation of the experiment time in the training data and the sensitivity parameters of the corresponding experiment samples, and reading the time sequence and the environmental conditions in the test data. Assuming that the mean value corresponding to a group of experimental samples obtained at the experimental time of the ith experiment in the training data is mu i Standard deviation of σ i 。
And the data correlation module is used for determining the relation between the time series and the mean value and the standard deviation of the sensitivity parameters. Specifically, the relationship between the mean value and the time series is judged through an Arrhenius model:
μ＝e a+b*T (1)
wherein mu is the mean value corresponding to the test sample, a and b are coefficients, and T is a time sequence.
And fitting the input data ln mu and the time sequence T by using a least square method to obtain coefficients a and b, so as to determine the relation between the time sequence and the mean value of the sensitivity parameter, as shown in fig. 2, the relation between the mean value mu obtained by fitting under the input example and the time sequence.
Meanwhile, the relation between the time series and the standard deviation of the sensitivity parameters is determined by utilizing segmented interpolation, and as shown in fig. 3, the relation between the standard deviation sigma obtained by fitting under the input example and the time series is determined.
The parameter calculation module is used for calculating parameters of a preset distribution function under the variable environment condition. Specifically, the relation between the parameters of the preset distribution function and the test sample is determined based on the relation between the time sequence and the mean value and the standard deviation of the sensitivity parameters, and the parameters of the preset distribution function under the variable environment condition are obtained. Preferably, the present implementation uses a normal distribution function as the preset distribution function.
And the probability calculation module is used for calculating the failure probability under the variable environmental condition. Specifically, the failure probability corresponding to the time point is calculated according to the fitted mean value mu and the standard deviation sigma. Giving a threshold value, judging that the sensitivity parameter is invalid when the sensitivity parameter exceeds the threshold value, wherein the distribution of each time point is normal distribution, and the probability of the invalid of each time point is shown as follows
Wherein f (t) is a probability density function of a preset distribution function, and threshold is a threshold.
It should be noted that the foregoing method embodiments are described as a series of acts or combinations for simplicity in description, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art will recognize that the embodiments described in this specification are preferred embodiments and that acts or modules referred to are not necessarily required for this application.
Claims (8)
1. A variable environment input life evaluation method based on a 71-degree method model is characterized by comprising the following steps:
s1, reading training data and test data: reading the mean value and the standard deviation of the experiment time in the training data and the sensitivity parameters of the corresponding experiment samples, and reading the time sequence and the environmental conditions in the test data;
s2, determining the relation between the time sequence and the mean value and standard deviation of the sensitivity parameters: fitting and solving the relation between the time sequence and the mean value of the sensitivity parameters by using a least square method and an Arrhenius model, and determining the relation between the time sequence and the standard deviation of the sensitivity parameters by using segmented interpolation;
s3, calculating parameters of a preset distribution function under the variable environment condition: determining the relation between the parameters of the preset distribution function and the test sample based on the relation between the time sequence and the mean value and the standard deviation of the sensitivity parameters to obtain the parameters of the preset distribution function under the variable environment condition;
s4, calculating the failure probability under the variable environmental condition: and determining an effective probability threshold value according to the sensitivity parameter, judging that the sensitivity parameter fails when the sensitivity parameter exceeds the threshold value, and integrating the probability density function of the preset distribution function under the variable environment condition according to the effective probability threshold value, thereby determining the failure probability.
2. The variable environment input life evaluation method based on the 71-degree method model according to claim 1, wherein in step S2, the relation between the mean value and the time series is judged through an Arrhenius model:
μ＝e a+b*T (1)
wherein mu is a mean value corresponding to the test sample, a and b are coefficients, and T is a time sequence;
and fitting the input data ln mu and the time sequence T by using a least square method to obtain coefficients a and b, thereby determining the relation between the time sequence and the mean value of the sensitivity parameters.
3. The variable environment input life evaluation method based on the 71-degree method model according to claim 1, wherein in step S4, the failure probability calculation formula is as follows:
wherein f (t) is a probability density function of a preset distribution function, and threshold is a threshold.
4. The variable environment input life assessment method based on the 71-degree method model according to any one of claims 1-3, wherein the preset distribution function comprises a normal distribution function.
5. A variable environment input life evaluation system based on a 71-degree method model is characterized by comprising the following steps:
the data reading module is used for reading the experiment time in the training data and the mean value and the standard deviation of the sensitivity parameters of the corresponding experiment samples, and reading the time sequence and the environmental conditions in the test data;
a data association module: fitting and solving the relation between the time sequence and the mean value of the sensitivity parameters by using a least square method and an Arrhenius model, and determining the relation between the time sequence and the standard deviation of the sensitivity parameters by using segmented interpolation;
the parameter calculation module is used for determining the relation between the parameters of the preset distribution function and the test sample based on the relation between the time sequence and the mean value and the standard deviation of the sensitivity parameters to obtain the parameters of the preset distribution function under the variable environment condition;
and the probability calculation module is used for determining an effective probability threshold value according to the sensitivity parameter, judging that the sensitivity parameter fails when the sensitivity parameter exceeds the threshold value, integrating the probability density function of the preset distribution function under the variable environment condition according to the judgment, and further determining the failure probability.
6. The variable environment input life evaluation system based on the 71-degree method model according to claim 5, wherein in the data association module, the relation between the mean value and the time series is judged through an Arrhenius model:
μ＝e a+b*T (3)
wherein mu is a mean value corresponding to the test sample, a and b are coefficients, and T is a time sequence;
and fitting the input data ln mu and the time sequence T by using a least square method to obtain coefficients a and b, thereby determining the relation between the time sequence and the average value of the sensitivity parameters.
7. The variable environment input life evaluation system based on the 71-degree method model according to claim 5, wherein in the probability calculation module, the failure probability calculation formula is as follows:
wherein f (t) is a probability density function of a preset distribution function, and threshold is a threshold.
8. The variable environment input life assessment system based on 71-degree method model according to any one of claims 5-7, wherein the preset distribution function comprises a normal distribution function.
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