【摘要】：目前,故障預測與健康管理(PHM)領域面臨著不斷提高的技術要求和不斷增長的應用需求,對于PHM中的可靠性預測、性能退化趨勢預測和剩余壽命預測的研究和探索也越來越受到重視。本論文以電子產(chǎn)品的可靠性、性能退化趨勢和剩余壽命的實時預測為核心研究課題,在文中重點探索了基于貝葉斯方法和滑動窗口樣本分組法的實時可靠性預測方法、基于可靠性試驗數(shù)據(jù)的實時性能退化趨勢預測方法、基于差異分析和相似性的實時剩余壽命預測方法,核心內容分為四個組成部分。第一部分將研究分析基于貝葉斯方法和滑動窗口樣本分組法的可靠性實時預測方法。通過使用可靠性試驗獲取的歷史退化數(shù)據(jù)和實時采集測量獲取到的現(xiàn)場退化數(shù)據(jù),基于貝葉斯方法,將現(xiàn)場退化數(shù)據(jù)融入與歷史退化數(shù)據(jù)中,利用滑動窗口樣本分組法,更新性能參數(shù)變量分布的時變參數(shù),計算出偽失效壽命,由此進一步得到產(chǎn)品的可靠性的實時預測結果。這種方法適用于歷史數(shù)據(jù)的數(shù)量有限但并不缺乏的情況下,可以在最大程度上利用到有限的現(xiàn)場數(shù)據(jù)信息,得到準確有效的實時可靠性預測信息。第二部分將研究分析基于可靠性試驗退化數(shù)據(jù)的性能退化趨勢實時預測方法。通過利用現(xiàn)場退化數(shù)據(jù)和可靠性試驗退化數(shù)據(jù)之間的關系,運用差異分析理論,分別獲得由現(xiàn)場退化數(shù)據(jù)以及融合了現(xiàn)場退化數(shù)據(jù)和可靠性試驗退化數(shù)據(jù)的數(shù)據(jù)集運算得到的趨勢預測結果,然后根據(jù)預測結果的曲線擬合誤差計算兩者的權值并進行數(shù)據(jù)融合,最終得到產(chǎn)品的性能退化趨勢實時預測結果。這種方法相較于基于現(xiàn)場數(shù)據(jù)時間序列的性能退化趨勢的預測方法,適用范圍更廣,可以提供更加準確、更加穩(wěn)定的預測結果。第三部分將研究分析基于差異分析和相似性的剩余壽命實時預測方法。將可靠性試驗退化數(shù)據(jù)分成若干組,運用差異分析理論,將每一組可靠性試驗退化數(shù)據(jù)分別與現(xiàn)場退化數(shù)據(jù)進行比較分析,并得到若干個剩余壽命預測結果,根據(jù)每一組可靠性試驗退化數(shù)據(jù)與現(xiàn)場退化數(shù)據(jù)之間的相似度分配權重值,將若干個剩余壽命預測結果融合成最終的剩余壽命實時預測結果。這種方法不需要針對退化數(shù)據(jù)進行數(shù)學建模,對退化數(shù)據(jù)的軌跡類型和統(tǒng)計分布特性沒有依賴,對有著很強的適用性。同時又能彌補基于相似性的方法所存在的缺陷,能夠進一步提升剩余壽命的預測效果。第四部分將展示一個利用VC6.0和MATCOM編程的軟件,主要用于驗證前三部分中提到的基于貝葉斯方法和滑動窗口樣本分組法的可靠性實時預測方法、基于可靠性試驗退化數(shù)據(jù)的性能退化趨勢實時預測方法和基于差異分析和相似性的剩余壽命實時預測方法。
[Abstract]:At present, the field of fault prediction and health management (PHM) is faced with increasing technical requirements and increasing application requirements, for reliability prediction in PHM, More and more attention has been paid to the research and exploration of performance degradation trend prediction and residual life prediction. This paper focuses on the real-time prediction of reliability, performance degradation trend and residual life of electronic products. In this paper, the real-time reliability prediction method based on Bayesian method and sliding window sample grouping method is explored. The real-time performance degradation trend prediction method based on reliability test data and the real-time residual life prediction method based on difference analysis and similarity are divided into four parts. In the first part, the reliability real-time prediction method based on Bayesian method and sliding window sample grouping method is studied. By using the historical degradation data obtained from reliability test and the field degradation data obtained by real-time acquisition and measurement, based on Bayesian method, the field degradation data is integrated into the historical degradation data and the sliding window sample grouping method is used. The time-varying parameters of the distribution of performance parameters are updated, and the pseudo-failure life is calculated, and the real-time prediction results of the reliability of the products are obtained. This method can be used to obtain accurate and effective real-time reliability prediction information by using the limited field data to the maximum extent when the number of historical data is limited but not lacking. In the second part, the real-time prediction method of performance degradation trend based on reliability test degradation data is studied. By using the relationship between field degradation data and reliability test degradation data, the difference analysis theory is used. The trend prediction results obtained from field degradation data and data set operations that combine field degradation data and reliability test degradation data are obtained, respectively, Then according to the curve fitting error of the prediction results, the weights of the two are calculated and data fusion is carried out, and finally the real-time prediction results of the performance degradation trend of the products are obtained. Compared with the performance degradation trend prediction method based on field data time series, this method can provide more accurate and stable prediction results. In the third part, we analyze the real-time prediction method of residual life based on difference analysis and similarity. The degradation data of reliability test are divided into several groups, and each group of degradation data of reliability test is compared with field degradation data by using the theory of difference analysis, and a number of residual life prediction results are obtained. According to the similarity between each set of reliability test degradation data and the field degradation data, several residual life prediction results are fused into the final residual life real-time prediction results. This method does not need to do mathematical modeling for degenerate data and has no dependence on the trace type and statistical distribution characteristics of degraded data. It has strong applicability to the degenerate data. At the same time, it can make up for the defects of the similarity based method, and can further improve the prediction effect of residual life. The fourth part will show a software which is programmed by VC6.0 and MATCOM, which is mainly used to verify the reliability real-time prediction method based on Bayesian method and sliding window sample grouping method mentioned in the previous three parts. Performance degradation trend real-time prediction method based on reliability test degradation data and residual life real-time prediction method based on difference analysis and similarity.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB114.3

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相關期刊論文 前1條

1 尤明懿;;一個拓展的基于相似性的剩余壽命預測框架[J];電子產(chǎn)品可靠性與環(huán)境試驗;2012年03期

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本文編號：2256301