The Algorithm’s Design And Implementation Of Reliability And Remaining Life Real-time Prediction For Electronic Systems

Nowadays, the field of Prognostic and Health Management(PHM) begin to face the higher technical and application requirements. As the result, the research and exploration of reliability prediction, performance degradation trend prediction and remaining life prediction are put a higher and higher value on. This paper’s main research objects are the reliability, the degradation trend and residual life real-time prediction of electronics system. The key points are real-time reliability prediction method based on Bayes method and sliding time series samples dividing method, real-time performance degradation trend prediction method based on reliability experimental data and real-time remaining life prediction method based on difference analysis and similarity. This paper’s prime contents will be divided into four parts.The first part will research and analysis the real-time reliability prediction method based on Bayes method and sliding series samples dividing method. This part adopts the Bayes method by using history degradation data and the real-time on-site data, fuses the on-site data into history data, utilizes sliding series samples dividing method updates the performance parameter of variable’s distribution and calculates pseudo-failure lifetime to get the real-time reliability result. This method is suitable for the condition that limited history data but not lacking data. This method can utilize the limited on-site data at the largest extent and acquire accurate and effect real-time reliability prediction information.The second part will research and analysis the real-time performance degradation trend prediction method based on reliability experimental data. The relationship between the field data and reliability experimental data and difference analysis theory can be used to acquire the two trend prediction results by on-site degradation data and data sets including on-site degradation data and reliability experimental data respectively. Then two prediction results can be fused according to weights calculated by two prediction results’ error and finally get the real-time performance trend degradation prediction results. This method can provide a better applicability, more accurate and stable prediction result comparing to the prediction method based on on-site data time series.The third part will research and analysis the real-time remaining life prediction method based on difference analysis and similarity. Firstly, reliability experimental data is divided into some groups. Then use difference analysis theory to compare the on-site data and reliability experimental data of every group so that we can get some remaining life prediction results. According to the similarity between on-site degradation data and reliability experimental data of every group, we can allocate the weights to every group to fuse prediction results to acquire final remaining life prediction results. There is no need to build mathematic model for degradation data about this method. As the result, this method is independent to the track type and the statistical distribution characteristics of degradation data with a great applicability. In the same time, it can offset the flaw of the method based on similarity and promote the residual life prediction effect further more.The forth part will show software developed by VC6.0 and MATCOM which is mainly used to verify the former three methods which are real-time reliability prediction method based on Bayes method and sliding series samples dividing method, real-time performance degradation trend prediction method based on reliability experimental data, real-time remaining life prediction method based on difference analysis and similarity.