Statistical learning modeling based health indicator construction for machine condition monitoring

Machine condition monitoring aims to evaluate machine health conditions by analyzing machine vibration signals, which is helpful to make timely maintenance decisions and prevent unexpected accidents. Currently, constructions of virtual and physical health indicators (HIs) are commonly used methods for machine condition monitoring. However, most classic physical and virtual HIs lack inherent thresholds, robustness, monotonicity, and interpretability for machine condition monitoring. In this paper, a statistical learning modeling based HI construction method for machine condition monitoring is proposed to solve these problems. Firstly, a statistical decision theory is suggested to clearly describe a machine condition monitoring objective, and subsequently shapes of square envelope spectra are robustly modeled by using a parametric statistical model called a penalized B-spline approximation. Further, an interpretable HI named B-spline weight HI (BSWHI) as well as an inherent statistical threshold is accordingly constructed based on the Mahalanobis distance between B-spline weights of testing samples and a healthy sample. Experiments on bearing and gear run-to-failure datasets are studied to show that the proposed BSWHI and its inherent statistical threshold can effectively detect early machine faults and simultaneously provide monotonic degradation assessment trends. The proposed interpretable BSWHI has achieved a substantial improvement over existing classic HIs.