Techniques of trend analysis in degradation-based prognostics

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• 作者：Seyed A. Niknam ; John Kobza ; J. Wesley Hines
• 关键词：Prognostics ; Degradation ; Monotonicity ; General path models ; Acoustic emission
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：88
• 期：9-12
• 页码：2429-2441
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design;
• 出版者：Springer London
• ISSN：1433-3015
• 卷排序：88

文摘

Monotonic fault progression is an important assumption for a number of prognostic models. This assumption can be violated through human intervention and self‐healing and result in non-monotonic degradation data which not only increases the uncertainty but also may cause model failure. Methods to analyze and handle non-monotonic degradation in repairable systems are practically nonexistent in the literature. In this research, we intend to consider repairable systems in which self‐healing is possible and human interventions are desirable. We presented a novel example of self-healing for fatigue cracks analyzed by acoustic emission. The aim of the present paper is to initiate a new research area on using non-monotonic measures in degradation-based prognostics. However, this research is not a review of trend analysis techniques, and therefore, there are more techniques to be considered or developed in future studies. In effect, trend analysis should be considered as an integral part of prognostics and health management. This study considers trend analysis for three classes of data, (1) prognostic parameters, (2) degradation waveform, and (3) multivariate data. A new form of crest factor is introduced for more effective waveform analysis of non-monotonic data. In addition, two algorithms are introduced to treat non-monotonic trend. The prognostic model used in this research does not produce results without treating non-monotonicity. These kinds of algorithm have promising potential to treat non-monotonicity and deal with arbitrary stationary noise in degradation data.