CN103310051B - Board information terminal Failure Rate Forecasting Method in a kind of life cycle management - Google Patents
Board information terminal Failure Rate Forecasting Method in a kind of life cycle management Download PDFInfo
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Abstract
Board information terminal Failure Rate Forecasting Method in a kind of life cycle management of the present invention, belongs to forecasting technique in life span field, the prediction of the board information terminal failure rate in especially a kind of life cycle management.Comprise 1) determine board information terminal life cycle management; 2) board information terminal failure criterion is determined; 3) synthetic chemistry laboratory speedup factor model is constructed; 4) synthetic chemistry laboratory high-acceleration life test data is gathered; 5) failure rate model is constructed; 6) board information terminal Performance Degradation Model is built in conjunction with failure rate model; 7) structure is based on the life-span dynamic prediction model of gray theory; 8) failure rate and the residual life of the real time node under board information terminal life cycle management is predicted.The present invention by systematically applying the environmental stress that increases gradually and working stress, shorten sense cycle to excite fault, the weak link exposed in design; By analyzing the performance condition of each operation, provide the reliability index of each operation of product.
Description
Technical Field
The invention discloses a method for predicting the fault rate of a vehicle-mounted information terminal in a full life cycle, belongs to the technical field of life prediction, and particularly relates to the prediction of the fault rate of the vehicle-mounted information terminal in the full life cycle.
Background
Under normal operating conditions, life testing methods are often employed to estimate various reliability characteristics of the product. This method is not a suitable method for products having a particularly long lifetime. Because it needs to spend a long test time, even the life test can not be done, a new product is designed, and an old product is eliminated. This method is therefore incompatible with the rapid development of products.
The accelerated life test is an effective way for rapidly evaluating the service life and reliability index of a long-life high-reliability product. The key to evaluating the life characteristics of a product at normal stress levels using accelerated life test data is to establish a relationship between the life characteristics and the stress levels.
Currently, in the research on the acceleration model, the single stress acceleration model is relatively mature. However, the environmental stresses to which the product is subjected in actual use are complex, such as being affected by stresses such as temperature, moderation and electrical stress. In fact, it is the combined effect of these stresses that affects the life of the product. Therefore, the comprehensive stress is introduced in the accelerated test, so that the test time can be shortened, the test efficiency can be improved, the actual environmental condition can be simulated more accurately, and a more credible result can be obtained.
The grey system theory is a new method for researching uncertainty problems of 'small samples' and 'poor information', and correct description of the system running state and the evolution law is achieved through generation, development and extraction of useful information of 'part' of known information.
The grey prediction was achieved by building a GM model (GreyModel). The most widely used of these is the GM (1,1) model for an equation, a first order variable. The GM (1,1) model is based on a random original time series, and a new time series is formed after the time accumulation. The law presented can be approximated with a solution of a first order linear differential equation, and the original time series revealed by the approximation of the solution of the first order linear differential equation is exponentially varying. Is provided withThe original time series data are:
accumulating and generating the sequence to obtain a 1-AGO (accumulated Generation operation) sequence as follows:
wherein,
establishing a whitening equation:
and (3) carrying out subtraction reduction on the solution of the whitening equation to obtain a gray prediction model:
when prediction is performed by given historical data (white system), the result of short-term prediction is closer to actual data because part of information of 'grey system' is known; when long-term prediction is carried out according to historical data, because the information of the black system is completely unknown and the number of prediction steps is too large, the internal evolution rule between the historical data and the black system becomes relatively fuzzy, and the deviation between the prediction result and an actual data curve becomes large.
Disclosure of Invention
The invention aims to provide a vehicle-mounted information terminal fault rate prediction method in a whole life cycle aiming at the defects, regular failure time data obtained through preprocessing is used as a training sample of a performance degradation model, data of gray accumulation generation operation is processed by adopting a three-point sliding average method, the rules are automatically summarized from test data through self-learning of the gray prediction model, unknown information is predicted by the summarized rules, and the service life and fault rate information of the vehicle-mounted information terminal under the normal stress level can be obtained after the output information is predicted and subjected to inverse transformation. Establishing a mathematical model of the full life cycle by predicting the full life cycle of the electronic product and the residual life of each working period; through comprehensive environmental stress strengthening tests, a fault rate model in the whole life cycle is established, and the fault rate of each working period of the vehicle-mounted information terminal is predicted.
A method for predicting the failure rate of a vehicle-mounted information terminal in a whole life cycle is realized by adopting the following technical scheme, and comprises the following steps:
1) determining a full life cycle of a vehicle-mounted information terminal
And determining the whole life cycle of the vehicle-mounted information terminal as the working time of the vehicle-mounted information terminal from the use time of the vehicle-mounted information terminal or recovering the vehicle-mounted information terminal to work after renovation until the vehicle-mounted information terminal is in the limit state.
2) Determining fault criterion of vehicle-mounted information terminal
The faults of the vehicle-mounted information terminal are divided into hard faults and soft faults, wherein the hard faults indicate that one vehicle-mounted information terminal cannot execute one or more pre-designed functions during and after disturbance is applied, and normal work of the vehicle-mounted information terminal cannot be recovered if components in the vehicle-mounted information terminal are not repaired or replaced.
The soft fault indicates that one vehicle-mounted information terminal cannot execute one or more pre-designed functions during disturbance application, but can be recovered to a normal operation state after disturbance application is stopped.
3) Model for constructing comprehensive environment stress acceleration factor
And carrying out a high-acceleration service life test on the vehicle-mounted information terminal by using comprehensive environmental stress, wherein the comprehensive environmental stress comprises temperature, humidity and electric stress.
The acceleration factor model for temperature is calculated as follows:
wherein,which represents the absolute temperature of the room temperature,
which represents the absolute temperature at a high temperature,
ea represents the activation energy (eV) of the deactivation reaction,
k represents boltzmann's constant.
The acceleration factor model for humidity is calculated as follows:
wherein,it is meant that the relative humidity of the accelerated test,
indicating a normal operating relative humidity level of the air conditioner,
n represents an acceleration rate constant of humidity.
The acceleration factor model of electrical stress is calculated as follows:
wherein,in order to achieve a high electrical stress,is normal electrical stress, and c is constant.
The high accelerated life test is carried out under the comprehensive environmental stress of various combinations of temperature, humidity, electric stress and the like, and the comprehensive accelerated factor of the test is obtained by multiplying the applied environmental stress accelerated factor.
4) Collecting comprehensive environmental stress high-acceleration service life test data
In the high-acceleration service life test process, the fault time point data of the vehicle-mounted information terminal in the test process is collected through a real-time communication log provided by an enterprise operation platform.
5) Constructing a failure rate model
The fault rate model function constructed is:
assuming that the number of samples participating in the above test protocol is M, the acceleration factor at each temperature stage isThe test time of the ith sample at each temperature stage isThe actual working time isWhen the actual working time under the high environmental stress is equal to the test time multiplied by the acceleration factor under the current environmental stress, the actual working time of the ith sample is
(4)
Actual working time of M samplesAnd summing to obtain the total working time T, wherein the actual total working time calculation formula of the M samples is as follows:
(5)
in the testing process, after sample data abnormity or indicator lamp abnormity is found, fault diagnosis and online restart are carried out by a remote debugging method, and if the problem is solved by remote diagnosis, the fault is recorded for 1 time; if n samples have faults, recording the faults for n times; in case of confirming that the remote cannot be solved, recording the total number F of faultsiThe rest of the tests were continued, after which the test time for this faulty sample was not calculated as the working time. After the test was completed, the number of failures for all samples was:
therefore, the fault rate function of the vehicle-mounted information terminal is constructed as follows:
（6）
6) performance degradation model of vehicle-mounted information terminal built by combining fault rate model
And testing by adopting a step stress accelerated life test, wherein the step stress accelerated life test is to start the test of the sample from a low stress level, increase the stress level and the test time at a constant speed to a high stress level, increase the stress level and the test time step by step, and stop the test until the terminal reaches the limit of the stress level. The method is used for testing the time from the test start to the complete failure of the sample, calculating the product performance parameters corresponding to each stepped stress stage, drawing a sample performance evaluation analysis curve by using the parameters and Matlab software, and analyzing the performance of the working condition of the vehicle-mounted information terminal.
In the testing process by adopting the stepping stress accelerated life test, all samples need to be electrified, and the connection between the samples and the center of an enterprise platform in the whole process is ensured to be normal; once the data of individual samples are found to be abnormal or the indicator lamps are found to be abnormal, fault diagnosis and online restart are carried out by a remote debugging method, and if the problem is solved by remote diagnosis, the fault is recorded for 1 time; if n samples have faults, recording the faults for n times; in the event that a remote failure is confirmed, the total number of failures is recorded and the remaining tests are continued, after which the test time for the failed sample is not calculated as the working time.
And (3) analyzing the performance of the working condition of the vehicle-mounted information terminal, and calculating the performance value of the actual time node of the vehicle-mounted information terminal according to a formula (7).
（7）
Wherein,for each stress node's performance value, the calculation formula is as follows:
（8）
is shown asiTest time at each stress phase;
is shown asiA comprehensive environmental stress acceleration factor at each stress stage;
7) dynamic life prediction model based on grey theory
And (4) performing further prediction analysis on the performance degradation result obtained in the step 6) by adopting a life dynamic prediction model based on a grey theory.
The specific implementation process of the lifetime dynamic prediction model based on the grey theory is as follows:
7.1) predicting in the step 1, and knowing the actual value of the point N-1 at the T-N +1 to T-1 time before the T timeInputting the actual value of the N-1 point into a grey prediction model to obtain a predicted value at the time T；
7.2) step 2 prediction, namely predicting the predicted T-time performance valueReturning the input end of the model as the last point of the N-1 point time sequence sliding window, removing the performance value at the T-N +1 moment, and recombining the performance values into the N-1 point time sequence sliding windowInput deviceGrey prediction model to obtain the predicted value at T +1 moment；
7.3) repeating the prediction until the nth step prediction is carried out, and combining a new N-1 point time sequence sliding windowInputting a grey prediction model to obtain a predicted value at the time T +1。
And predicting by adopting a grey theory-based life dynamic prediction model, so that a prediction sequence including the whole trend can be obtained, and meanwhile, the residual life of the vehicle-mounted information terminal in each working period is obtained.
When a dynamic life prediction model based on a grey theory is constructed, the original data needs to be preprocessed so as to adjust the original change situation of the data and restore the essential characteristics of the system to the maximum extent, thereby weakening the fluctuation change of the data.
The preprocessing can adopt a three-point moving average method to process data, and newly generated data are as follows:
（9）
therein is provided withnA known data
（10）
In the development of any gray system, there will be a constant number of random perturbations or drivers into the system over time that affect the development of the system. The farther into the future, the farther away from the time origin, the weaker the predictive significance of the GM (1,1) model. Therefore, in practical applications, data entering the system over time must be considered continuously, and new data must be placed into the model at any time to build a new prediction model.
8) Predicting failure rate and residual life of actual time node under full life cycle of vehicle-mounted information terminal
By calculating the comprehensive environmental stress acceleration factor of each temperature stage and the performance evaluation value of each time node of the vehicle-mounted information terminalAnd predicting the residual service life of the vehicle-mounted information terminal under each working condition by adopting a service life dynamic prediction model based on a grey theory, calculating the fault rate under the corresponding working condition through a formula (6), and combining the results to obtain the residual service life and the fault rate under the real-time working condition in the whole service life cycle of the vehicle-mounted information terminal.
The invention systematically applies gradually increased environmental stress and working stress, shortens the detection period to excite faults and expose weak links in the design; through analyzing the performance condition of each working period, the reliability index of each working period of the product is provided, and the method has the following advantages:
1) and a high-acceleration life test scheme is designed, so that the time for an acceleration life test of an electronic product is shortened remarkably.
2) And (3) establishing a dynamic life prediction model by adopting a grey prediction theory, and mastering the residual life of the product in each working period.
3) And establishing a fault rate model by combining the performance conditions of the vehicle-mounted information terminal in each working period, and predicting the fault rate of the vehicle-mounted information terminal in the whole life cycle.
4) The method for predicting the failure rate of the vehicle-mounted information terminal in the whole life cycle is suitable for small sample test data and is convenient for practical engineering application.
5) The vehicle-mounted information terminal fault rate prediction method in the whole life cycle has stronger engineering applicability and universality for different vehicle-mounted information terminals or stress types.
Detailed Description
The method for predicting the failure rate of the vehicle-mounted information terminal in the whole life cycle comprises the following steps:
1) determining a full life cycle of a vehicle-mounted information terminal
And determining the whole life cycle of the vehicle-mounted information terminal as the working time of the vehicle-mounted information terminal from the use time of the vehicle-mounted information terminal or recovering the vehicle-mounted information terminal to work after renovation until the vehicle-mounted information terminal is in the limit state.
2) Determining fault criterion of vehicle-mounted information terminal
The faults of the vehicle-mounted information terminal are divided into hard faults and soft faults, wherein the hard faults indicate that one vehicle-mounted information terminal cannot execute one or more pre-designed functions during and after disturbance is applied, and normal work of the vehicle-mounted information terminal cannot be recovered if components in the vehicle-mounted information terminal are not repaired or replaced.
The soft fault indicates that one vehicle-mounted information terminal cannot execute one or more pre-designed functions during disturbance application, but can be recovered to a normal operation state after disturbance application is stopped.
3) Model for constructing comprehensive environment stress acceleration factor
And carrying out a high-acceleration service life test on the vehicle-mounted information terminal by using comprehensive environmental stress, wherein the comprehensive environmental stress comprises temperature, humidity and electric stress.
The acceleration factor model for temperature is calculated as follows:
wherein,which represents the absolute temperature of the room temperature,
which represents the absolute temperature at a high temperature,
ea represents the activation energy (eV) of the deactivation reaction,
k represents boltzmann's constant.
The acceleration factor model for humidity is calculated as follows:
wherein,it is meant that the relative humidity of the accelerated test,
indicating a normal operating relative humidity level of the air conditioner,
n represents an acceleration rate constant of humidity.
The acceleration factor model of electrical stress is calculated as follows:
wherein,in order to achieve a high electrical stress,is normal electrical stress, and c is constant.
The high accelerated life test is carried out under the comprehensive environmental stress of various combinations of temperature, humidity, electric stress and the like, and the comprehensive accelerated factor of the test is obtained by multiplying the applied environmental stress accelerated factor.
4) Collecting comprehensive environmental stress high-acceleration service life test data
In the high-acceleration service life test process, the fault time point data of the vehicle-mounted information terminal in the test process is collected through a real-time communication log provided by an enterprise operation platform.
5) Constructing a failure rate model
The fault rate model function constructed is:
assuming that the number of samples participating in the above test protocol is M, the acceleration factor at each temperature stage isThe test time of the ith sample at each temperature stage isThe actual working time isHigh environmental stressThe actual working time of the ith sample is equal to the test time multiplied by the acceleration factor under the current environmental stress
(4)
Actual working time of M samplesAnd summing to obtain the total working time T, wherein the actual total working time calculation formula of the M samples is as follows:
(5)
in the testing process, after sample data abnormity or indicator lamp abnormity is found, fault diagnosis and online restart are carried out by a remote debugging method, and if the problem is solved by remote diagnosis, the fault is recorded for 1 time; if n samples have faults, recording the faults for n times; in case of confirming that the remote cannot be solved, recording the total number F of faultsiThe rest of the tests were continued, after which the test time for this faulty sample was not calculated as the working time. After the test was completed, the number of failures for all samples was:
therefore, the fault rate function of the vehicle-mounted information terminal is constructed as follows:
（6）
6) performance degradation model of vehicle-mounted information terminal built by combining fault rate model
And testing by adopting a step stress accelerated life test, wherein the step stress accelerated life test is to start the test of the sample from a low stress level, increase the stress level and the test time at a constant speed to a high stress level, increase the stress level and the test time step by step, and stop the test until the terminal reaches the limit of the stress level. The method is used for testing the time from the test start to the complete failure of the sample, calculating the product performance parameters corresponding to each stepped stress stage, drawing a sample performance evaluation analysis curve by using the parameters and Matlab software, and analyzing the performance of the working condition of the vehicle-mounted information terminal.
The specific test parameter settings are shown in table 1, based on the designed test protocol parameters.
TABLE 1 test parameters
| Test time | Temperature/humidity |
| 24 hours | 25℃+60% |
| 24 hours | 35℃+65% |
| 24 hours | 45℃+70% |
| 24 hours | 55℃+75% |
| 24 hours | 65℃+80% |
| 24 hours | 75℃+85% |
| 24 hours | 85℃+90% |
| 24 hours | 95℃+95% |
In the testing process by adopting the stepping stress accelerated life test, all samples need to be electrified, and the connection between the samples and the center of an enterprise platform in the whole process is ensured to be normal; once the data of individual samples are found to be abnormal or the indicator lamps are found to be abnormal, fault diagnosis and online restart are carried out by a remote debugging method, and if the problem is solved by remote diagnosis, the fault is recorded for 1 time; if n samples have faults, recording the faults for n times; in the event that a remote failure is confirmed, the total number of failures is recorded and the remaining tests are continued, after which the test time for the failed sample is not calculated as the working time.
And (3) analyzing the performance of the working condition of the vehicle-mounted information terminal, and calculating the performance value of the actual time node of the vehicle-mounted information terminal according to a formula (7).
（7）
Wherein,for each stress node's performance value, the calculation formula is as follows:
（8）
is shown asiTest time at each stress phase;
is shown asiA comprehensive environmental stress acceleration factor at each stress stage;
7) dynamic life prediction model based on grey theory
And (4) performing further prediction analysis on the performance degradation result obtained in the step 6) by adopting a life dynamic prediction model based on a grey theory.
From a prediction perspective, a dynamic prediction model is the most ideal model. With the development of the system, the information significance of the historical data is gradually reduced, old data is timely removed while new information is continuously supplemented, and the prediction model can reflect the current characteristics of the system better. In addition, old data is removed in time, the calculation amount can be reduced, and the method is favorable for actual operation. In addition, the grey parameters are corrected once every prediction step, and the prediction model is updated. Along with the continuous correction of the ash parameters, the model is gradually improved, so that the prediction precision is improved.
The specific implementation process of the lifetime dynamic prediction model based on the grey theory is as follows:
7.1) predicting in the step 1, and knowing the actual value of the point N-1 at the T-N +1 to T-1 time before the T timeInputting the actual value of the N-1 point into a grey prediction model to obtain a predicted value at the time T；
7.2) step 2 prediction, namely predicting the predicted T-time performance valueReturning the input end of the model as the last point of the N-1 point time sequence sliding window, removing the performance value at the T-N +1 moment, and recombining the performance values into the N-1 point time sequence sliding windowInputting a grey prediction model to obtain a predicted value at the time T +1；
7.3) repeating the prediction until the nth step prediction is carried out, and combining a new N-1 point time sequence sliding windowInputting a grey prediction model to obtain a predicted value at the time T +1。
And predicting by adopting a grey theory-based life dynamic prediction model, so that a prediction sequence including the whole trend can be obtained, and meanwhile, the residual life of the vehicle-mounted information terminal in each working period is obtained.
The model can supplement and utilize new information in time and improve the whitening degree of the gray interval. And the gray model parameters are corrected once every prediction step, the model is improved, and the constructed gray prediction model further improves the prediction accuracy.
When a dynamic life prediction model based on a grey theory is constructed, the original data needs to be preprocessed so as to adjust the original change situation of the data and restore the essential characteristics of the system to the maximum extent, thereby weakening the fluctuation change of the data.
The preprocessing can adopt a three-point moving average method to process data, and newly generated data are as follows:
（9）
therein is provided withnA known data
（10）
In the development of any gray system, there will be a constant number of random perturbations or drivers into the system over time that affect the development of the system. The farther into the future, the farther away from the time origin, the weaker the predictive significance of the GM (1,1) model. Therefore, in practical applications, data entering the system over time must be considered continuously, and new data must be placed into the model at any time to build a new prediction model.
8) Predicting failure rate and residual life of actual time node under full life cycle of vehicle-mounted information terminal
By calculating the comprehensive environmental stress acceleration factor of each temperature stage and the performance evaluation value of each time node of the vehicle-mounted information terminalAnd predicting the residual service life of the vehicle-mounted information terminal under each working condition by adopting a service life dynamic prediction model based on a grey theory, calculating the fault rate under the corresponding working condition through a formula (6), and combining the results to obtain the residual service life and the fault rate under the real-time working condition in the whole service life cycle of the vehicle-mounted information terminal.
Claims (4)
1. A method for predicting the failure rate of a vehicle-mounted information terminal in a whole life cycle is characterized by comprising the following steps:
1) determining a full life cycle of a vehicle-mounted information terminal
Determining the whole life cycle of the vehicle-mounted information terminal as the working time of the vehicle-mounted information terminal from the use time of the vehicle-mounted information terminal or recovering the vehicle-mounted information terminal to the limit state after the vehicle-mounted information terminal is repaired;
2) determining fault criterion of vehicle-mounted information terminal
The faults of the vehicle-mounted information terminal are divided into hard faults and soft faults, wherein the hard faults indicate that one vehicle-mounted information terminal cannot execute one or more pre-designed functions during and after disturbance is applied, and normal work of the vehicle-mounted information terminal cannot be recovered if components in the vehicle-mounted information terminal are not repaired or replaced;
the soft fault indicates that one vehicle-mounted information terminal cannot execute one or more pre-designed functions during disturbance application, but can be recovered to a normal operation state after disturbance application is stopped;
3) model for constructing comprehensive environment stress acceleration factor
Carrying out a high-acceleration life test on the vehicle-mounted information terminal by using comprehensive environmental stress, wherein the comprehensive environmental stress comprises temperature, humidity and electric stress;
the high accelerated life test is carried out under the combined comprehensive environmental stress of temperature, humidity and electric stress, and the comprehensive accelerated factor of the test is obtained by multiplying the applied environmental stress accelerated factor;
the temperature acceleration factor model is calculated as follows:
wherein,which represents the absolute temperature of the room temperature,
which represents the absolute temperature at a high temperature,
ea represents the activation energy (eV) of the deactivation reaction,
k represents a boltzmann constant;
the acceleration factor model for humidity is calculated as follows:
wherein,it is meant that the relative humidity of the accelerated test,
indicating a normal operating relative humidity level of the air conditioner,
n represents an acceleration rate constant of humidity;
the acceleration factor model of electrical stress is calculated as follows:
wherein,in order to achieve a high electrical stress,normal electrical stress, c is a constant;
4) collecting comprehensive environmental stress high-acceleration service life test data
In the high-acceleration service life test process, acquiring fault time point data of the vehicle-mounted information terminal in the test process through a real-time communication log provided by an enterprise operation platform;
5) constructing a failure rate model
The fault rate model function is constructed as
Assuming that the number of samples participating in the above test protocol is M, the acceleration factor at each temperature stage isThe test time of the ith sample at each temperature stage isIn fact workAt a time ofWhen the actual working time under the high environmental stress is equal to the test time multiplied by the acceleration factor under the current environmental stress, the actual working time of the ith sample is
(4)
Actual working time of M samplesAnd summing to obtain the total working time T, wherein the actual total working time calculation formula of the M samples is as follows:
(5)
in the testing process, after sample data abnormity or indicator lamp abnormity is found, fault diagnosis and online restart are carried out by a remote debugging method, and if the problem is solved by remote diagnosis, the fault is recorded for 1 time; if n samples have faults, recording the faults for n times; in case of confirming that the remote cannot be solved, recording the total number F of faultsiThe rest of the tests are continued, and the test time of the fault sample is not calculated as the working time; after the test was completed, the number of failures for all samples was:
thus, the failure rate function of the vehicle-mounted information terminal is constructed as
（6）
6) Performance degradation model of vehicle-mounted information terminal built by combining fault rate model
Testing by adopting a stepping stress accelerated life test, testing the time of a sample from the beginning of the test to complete failure by the method, calculating the performance parameters of the product corresponding to each stepping stress stage, drawing a sample performance evaluation analysis curve by utilizing the parameters and Matlab software, and analyzing the performance of the working condition of the vehicle-mounted information terminal;
in the testing process by adopting the stepping stress accelerated life test, all samples need to be electrified, and the connection between the samples and the center of an enterprise platform in the whole process is ensured to be normal; once the data of individual samples are found to be abnormal or the indicator lamps are found to be abnormal, fault diagnosis and online restart are carried out by a remote debugging method, and if the problem is solved by remote diagnosis, the fault is recorded for 1 time; if n samples have faults, recording the faults for n times; under the condition that the remote situation can not be solved, recording the total times of the faults, continuing the rest tests, and calculating the test time of the fault sample as the working time;
analyzing the performance of the working condition of the vehicle-mounted information terminal, and calculating the performance value of the actual time node of the vehicle-mounted information terminal according to a formula (7);
（7）
wherein,for each stress node's performance value, the calculation formula is as follows:
（8）
is shown asiTest time at each stress phase;
is shown asiA comprehensive environmental stress acceleration factor at each stress stage;
7) dynamic life prediction model based on grey theory
Performing further prediction analysis on the performance degradation result obtained in the step 6) by adopting a life dynamic prediction model based on a grey theory;
the method adopts a grey theory-based dynamic life prediction model for prediction, can obtain a prediction sequence including the whole trend, and simultaneously obtains the residual life of each working period of the vehicle-mounted information terminal;
8) predicting failure rate and residual life of actual time node under full life cycle of vehicle-mounted information terminal
By calculating the comprehensive environmental stress acceleration factor of each temperature stage and the performance evaluation value of each time node of the vehicle-mounted information terminalAnd predicting the residual life of the vehicle-mounted information terminal under each working condition by adopting a life dynamic prediction model based on a grey theory, calculating the fault rate under the corresponding working condition through a formula (6), and combining the results to obtain the residual life and the fault rate under the real-time working condition in the whole life cycle of the vehicle-mounted information terminal.
2. The method for predicting the failure rate of the telematics terminal in the whole life cycle according to claim 1, wherein the step stress accelerated life test in the step 6) is to start the test on the sample from a low stress level, increase the stress level and the test time at the same speed to a high stress level, and increase the stress level and the test time step by step until the terminal reaches the limit of the stress level, so that the test is stopped.
3. The method for predicting the failure rate of the vehicle-mounted information terminal in the whole life cycle according to claim 1, wherein the specific implementation process of the dynamic life prediction model based on the gray theory in the step 7) is as follows:
7.1) predicting in the step 1, and knowing the actual value of the point N-1 at the T-N +1 to T-1 time before the T timeInputting the actual value of the N-1 point into a grey prediction model to obtain a predicted value at the time T；
7.2) step 2 prediction, namely predicting the predicted T-time performance valueReturning the input end of the model as the last point of the N-1 point time sequence sliding window, removing the performance value at the T-N +1 moment, and recombining the performance values into the N-1 point time sequence sliding windowInputting a grey prediction model to obtain a predicted value at the time T +1；
7.3) repeating the prediction until the nth step prediction is carried out, and combining a new N-1 point time sequence sliding windowInputting a grey prediction model to obtain a predicted value at the time T +1。
4. The method for predicting the failure rate of the vehicle-mounted information terminal in the whole life cycle according to claim 1, wherein in the step 7), when a dynamic life prediction model based on a gray theory is constructed, the original data needs to be preprocessed so as to adjust the original change situation of the data and restore the essential characteristics of the system to the maximum extent, so that the fluctuation change of the data is weakened;
the preprocessing adopts a three-point moving average method to process data, and newly generated data are as follows:
（9）
therein is provided withnA known data
（10）
。
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