A nonparametric health index and its statistical threshold for machine condition monitoring. (1st January 2021)

• Record Type: Journal Article
• Authors: Zhong, Jingjing; Wang, Dong; Li, Chuan
• Is Part Of: Measurement. Volume 167(2021)
• Publication Date: 2021-01-01
• Subjects: Nonparametric health index -- Machine condition monitoring -- Significance level -- Statistical threshold; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108290
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Statistical modeling and statistical analysis of normalized square envelope spectrum are proposed. A nonparametric health index and its statistical threshold are constructed for system health monitoring. Only a normal dataset is required in the statistical modeling and analysis. Box-Cox transformation of normalized square envelope spectrum is introduced to system health monitoring. The parameter of Box-Cox transformation is sensitive to bearing degradation. Abstract: Machine condition monitoring uses monitoring data to evaluate machine health conditions and conduct condition-based maintenance. Nowadays, kurtosis, entropy, Gini index and smoothness index are popular indices for machine condition monitoring and they fall into a unified framework. A problem is that, if monitoring data do not follow a particular assumed parametric health index, the parametric index is not fully useful for machine condition monitoring. Another problem is that parametric health indices lack their associate statistical thresholds at a significance level for machine condition monitoring. In this paper, statistical modeling and statistical analysis of normalized square envelope spectrum are proposed to construct a nonparametric health index and its associate statistical threshold at a significance level for machine condition monitoring. An illustrative bearing run-to-failure example showed that the proposed nonparametric health index and its statistical threshold can assess degradation well without needing a specific parametric form and abnormal and faulty datasets.