Combining Relevance Vector Machines and exponential regression for bearing residual life estimation

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• 作者：Francesco Di Maio^a ; ^{f.dimaio@mail.polimi.it} ; Kwok Leung Tsui^b ; Enrico Zio^a ; ^c ; ^{enrico.zio@ecp.fr} ; ^{enrico.zio@supelec.fr}
• 关键词：Prognostics ; Residual Useful Life ; Relevance Vector Machines ; Exponential regression ; Bayesian techniques
• 刊名：Mechanical Systems and Signal Processing
• 出版年：2012
• 期刊代码：189_08883270
• 类别：et
• 出版时间：August, 2012
• 卷：31
• 期：Complete
• 页码：405-427
• 文件大小：2001 K

摘要

In this paper we present a new procedure for estimating the bearing Residual Useful Life (RUL) by combining data-driven and model-based techniques. Respectively, we resort to (i) Relevance Vector Machines (RVMs) for selecting a low number of significant basis functions, called Relevant Vectors (RVs), and (ii) exponential regression to compute and continuously update residual life estimations. The combination of these techniques is developed with reference to partially degraded thrust ball bearings and tested on real world vibration-based degradation data. On the case study considered, the proposed procedure outperforms other model-based methods, with the added value of an adequate representation of the uncertainty associated to the estimates of the quantification of the credibility of the results by the Prognostic Horizon (PH) metric.