Affiliations: [a]
Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing, China
| [b] School of Software Engineering, Chongqing University, Chongqing, China
Correspondence:
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>Corresponding author. Bin Cai, School of Software Engineering, Chongqing University, Chongqing, China. E-mail: [email protected].
Abstract: Predicting the remaining useful life (RUL) of rolling element bearings (REBs) has emerged as a vital technique for guaranteeing the safety, availability, and efficiency of rotating machinery systems. An approach using locally linear fusion regression (LLFR) is developed for the RUL prediction of REBs. The original features, derived from the time domain and time– frequency domain of the vibration signal of the REBs, are extracted first. Utilizing locally linear embedding, the extracted features are then fused into a condition indicator reflecting the entire degradation process. The adaptive network-based fuzzy inference system is then introduced for the RUL prediction. The reported approach is investigated with real REB data. Peer models are employed to validate the performance of the proposed method in this work. The derived experimental results indicate that LLFR has superior prediction ability as compared to the peer models in terms of the introduced performance criteria and that it can obtain more reliable and precise prediction results.
Keywords: Remaining useful life, multi-feature fusion, regression, locally linear embedding, rolling element bearings
