Discovering the hidden health states in bearing vibration signals for fault prognosis

In recent years, there has been a growing interest in diagnosis and prognosis of motors and electrical drives. Effective and accurate prognosis and diagnosis of systems will eventually lead to condition based maintenance, which will decrease maintenance costs and system downtime. Much work has been done in diagnosing the state of a motor, however prediction of the health state at future times, and ultimately the prediction of the system´s remaining useful life (RUL), still proves to be a challenge. One of the challenges to efficient prognosis is that in many applications, there is no labeled training data and the different health states of the system are not known a priori. In this paper, we propose an approach for learning the hidden health states of a bearing from vibration signals. The proposed approach is based on extracting multiple features from sensor signals and identifying change points in the state of the system based on these features.