Research on the Prediction of the Remaining Life of Rolling Bearings Based on Long Short-Term Memory (LSTM)

Abstract

Rolling bearings are key components in rotating machinery. Their wear and failure can cause the entire equipment to stop operating. Therefore, it is extremely necessary to predict their remaining useful life (RUL). However, in reality, the wear of bearings does not change linearly and the uncertainty over time makes predicting RUL challenging. To address this issue, this paper proposes an end-to-end RUL prediction framework that combines multi-dimensional time-domain feature extraction with stacked long short-term memory (LSTM) networks. By extracting eight time-domain statistical indicators (such as RMS and Kurtosis), and using the sliding window mechanism to construct the feature sequence, it is input into the stacked LSTM model to capture the continuous degradation trajectory. The 15 bearings in the XJTU-SY benchmark dataset were evaluated against the ground-truth degradation targets. Experimental results show that the proposed framework can capture the degradation trends under different working conditions. The predictions are less accurate in the early stage, mainly because the time-domain features remain relatively stable during this period. However, the model provides reasonable overall accuracy and follows the degradation trend well in the later stage.