Development of Vibration-Based Health Indexes for Bearing Remaining Useful Life Prediction

Bearing failure can cause their host system shutdown, and even some catastrophic accidents. These will lead to a high maintenance cost and a huge economic loss. Thus, health monitoring and fault prognosis for bearings becomes increasingly important. Developing an effective health index (HI) will do help in these works. Hence, three different HIs are developed by using root mean square, Kolmogorov-Smirnov test, and Mahalanobis distance to reflect bearings' online health conditions. Four degradation models are constructed to estimate bearings remaining useful life (RUL) by using particle filter algorithm. Bearing life data are used to test the performance of fault prognostic approaches. Results show that all HIs reflect the degradation process of bearing effectively, and the proposed degradation model has the best performance in bearing RUL prediction.