A Method for Remaining Useful Life Prediction and Uncertainty Quantification of Rolling Bearings Based on Fault Feature Gain

Abstract

In the field of remaining useful life (RUL) prediction, accurately evaluating incipient faults in bearings by using conventional health indicators (HIs) poses challenges, while traditional neural network models fail to provide reliable uncertainty distributions for credible output. Therefore, a cutting-edge deep learning (DL) method based on fault feature gain (FFG) is proposed, which aims to accurately predict the RUL of rolling bearings while quantifying the associated uncertainty distribution. First, combined with the adaptive spectrum mode extraction (ASME) theory, FFG is proposed to quantitatively assess the degree of bearing damage. Second, a mechanism for identifying incipient faults is established to determine the optimal time for making the first prediction. Subsequently, a DL model combining gated recurrent unit (GRU) and Bayesian neural network (BNN) is constructed to predict the RUL of bearings and quantify the uncertainty distribution. Finally, experimental results obtained from an accelerated degradation test bench for rolling bearings validate the effectiveness and advantages of the proposed method. The results demonstrate that FFG enables accurate assessment of bearing health status while providing crucial insights into the underlying failure modes. Furthermore, the GRU-BNN model performs more accurately in RUL prediction and can better quantify the uncertainty of RUL.