Research on Bearing Remaining Useful Life Prediction Method Based on Double Bidirectional Long Short-Term Memory

Abstract

The predictive capability of traditional bearing remaining useful life (RUL) prediction models is insufficient, and the prediction networks lack universality, leading to unsatisfactory results in predicting the RUL of bearings, which leads to untimely maintenance decisions and significant economic losses. In order to solve this problem, this study employs Discrete Wavelet Transform (DWT) to denoise vibration signals and extract multi-domain features; the weighted averages of monotonicity, predictability, trendability, and robustness indicators are first ranked for selecting sensitive feature subsets as inputs for RUL prediction, feature fusion is conducted using the Kernel Principal Component Analysis (KPCA) method to obtain the health index (HI) of the bearing, and the failure threshold of the signal is determined based on the 3-sigma principle. An RUL prediction model, which combines Double Bidirectional Long Short-Term Memory (DBiLSTM) with attention mechanism (A-DBiLSTM), is then developed, and the Bayesian approach is used to adaptively search for network hyperparameters. Experiments were conducted using the PHM2012 dataset and the XJTU-SY dataset; the results indicate that the proposed RUL prediction model demonstrates higher predictive performance, exhibits satisfactory performance across different datasets, and possesses good generalization capability and applicability. This method further enhances the predictive capability of bearing RUL estimation.