An integrated approach to bearing fault diagnostics and prognostics

This paper presents an integrated fault diagnostic and prognostic approach for bearing health monitoring and condition-based maintenance. The proposed scheme consists of three main components including principal component analysis (PCA), hidden Markov model (HMM), and an adaptive stochastic fault prediction model. The principal signal features extracted by PCA are utilized by HMM to generate a component health/degradation index, which is the input to an adaptive prognostics component for on-line remaining useful life prediction. The effectiveness of the scheme is shown by simulation studies using experimental vibration data obtained from a bearing health monitoring testbed.