A nonparametric health index and its statistical threshold for machine condition monitoring

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.