Multistream sensor fusion-based prognostics model for systems with single failure modes

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• 作者：Xiaolei Fang ; Kamran Paynabar ; ^{kamran.paynabar@isye.gatech.edu} ; Nagi Gebraeel
• 关键词：Degradation modeling ; Functional variables selection ; (log)-location-scale regression ; Multivariate functional principal components analysis ; Signal fusion
• 刊名：Reliability Engineering & System Safety
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：159
• 期：Complete
• 页码：322-331
• 全文大小：1410 K
• 卷排序：159

文摘

Advances in sensor technology have facilitated the capability of monitoring the degradation of complex engineering systems through the analysis of multistream degradation signals. However, the varying levels of correlation with physical degradation process for different sensors, high-dimensionality of the degradation signals and cross-correlation among different signal streams pose significant challenges in monitoring and prognostics of such systems. To address the foregoing challenges, we develop a three-step multi-sensor prognostic methodology that utilizes multistream signals to predict residual useful lifetimes of partially degraded systems. We first identify the informative sensors via the penalized (log)-location-scale regression. Then, we fuse the degradation signals of the informative sensors using multivariate functional principal component analysis, which is capable of modeling the cross-correlation of signal streams. Finally, the third step focuses on utilizing the fused signal features for prognostics via adaptive penalized (log)-location-scale regression. We validate our multi-sensor prognostic methodology using simulation study as well as a case study of aircraft turbofan engines available from NASA repository.