A data-driven degradation prognostics approach for rolling element bearings

Abstract

Degradation prognostic plays a crucial role in increasing the efficiency of health management for rolling element bearings (REBs). In this paper, a novel four-step data-driven degradation prognostics approach is proposed for REBs. In the first step, a series of degradation features are extracted by analyzing the vibration signals of REBs in time domain, frequency domain and time-frequency domain. In the second step, three indicators are utilized to select the sensitive features. In the third step, different health state labels are automatically assigned for health state estimation, where the influence of uncertain initial condition is eliminated. In the last step, a multivariate health state estimation model and a multivariate multistep degradation trend prediction model are combined to estimate the residence time in different health status and remaining useful life (RUL) of REBs. Verification results using the XJTU-SY datasets validate the effectiveness of the proposed method and show a more accurate prognostics results compared with the existing major approaches.