Prognostics Analysis of Rolling Bearing Based on Bi-Directional LSTM and Attention Mechanism

Bearings as the key component of most rotating machinery, responsible for major breakdowns. Therefore, this paper addresses intelligent prognostics involving remaining useful life estimation. The proposed framework is based on a deep learning model to learn the bearing degradation from vibration responses. A comprehensive feature selection strategy involving PSO (particle swarm optimization) optimization technique and feature transformations is carried out. The sensitive prognostic features set are then input to BiLSTM (bi-directional long short-term memory) network to learn long-term time dependencies in two directions. Furthermore, the attention mechanism is integrated with BiLSTM enables selective processing of information. The experimental validation is carried out by acquiring data from a high-speed rotor supported on the bearings. The results achieved higher prediction accuracy. Also, the generalization on IEEE PHM data achieves higher RUL (remaining useful life) prediction accuracy as compared to state-of-art methods. Hence, the results proved the high performance and feasibility of the proposed RUL prognostic method.