An Integrated Approach for Bearing Health Indicator and Stage Division Using Improved Gaussian Mixture Model and Confidence Value

Abstract

Bearing prognosis is of paramount and practical importance for rotating machinery. In this article, we propose an integrated approach for bearing performance degradation assessment. First, we propose an improved clustering algorithm, called the Hellinger distance-based regularized Gaussian mixture model (HRGMM). In this model, the Hellinger distance is incorporated to measure the similarity between probability distributions (PDs) of raw data. The manifold regularized GMM is then enhanced to differentiate bearing performance changes. Second, we construct a new health indicator (HI) that combines the Jensen–Rényi divergence and improved confidence value to normalize the difference in PDs between the test condition and healthy condition. It aims to solve the problems of the insensitivity of HI to the incipient defect and its highly fluctuating behavior as the bearing deteriorates over time. Clustering results of the experimental and real bearings show that the HRGMM successfully distinguishes different health states, damage severities, and working conditions. The results of two run-to-failure bearing tests demonstrate that, for each bearing, the HI based on HRGMM drops in a smooth and monotonic manner and accurately identify the incipient defect as the beginning of the slight degradation. Moreover, it can automatically mark the sharp state declines as the beginning of severe degradation and failure stages so that appropriate warnings can be set for follow-up maintenance.