Entropy-maximization oriented interpretable health indicators for locating informative fault frequencies for machine health monitoring

Abstract

Machine health monitoring is an important domain to provide timely anomaly detection and diagnostic supports for condition based maintenance. Health indicator (HI) construction is an intuitive and efficient way to conduct continuous machine health monitoring. In this study, a maximization-entropy optimization oriented interpretable HI is proposed to locate informative fault frequencies for machine health monitoring. At first, a probabilistic HI is defined based on the sum of weighted spectral amplitudes in normalized square envelope spectrum (NSES) and its newly defined constraint. Next, different from maximization of negative entropy or minimization of entropy for extracting repetitive transients caused by localized faults, this paper provides a new perspective for integrating entropy maximization with maximum sparsity weights to propose a novel convex optimization degradation modeling methodology for construction of HIs. Besides the new perspective of entropy maximization, the novelty of this study lies in that optimized model weights can automatically locate fault characteristic frequencies in the NSES without any prior knowledge so that the probabilistic HI is sensitive to incipient abnormality. Based on the analysis of several run-to-failure bearing datasets, it is demonstrated the effectiveness and superiority of the proposed methodology for machine health monitoring and incipient fault diagnosis.