Deep-learning-assisted analysis and prediction for detecting the bearing degradation

In the slow-change degradation process of rolling bearings, there is a coupling relationship between the evolution of bearing failure and the onset of degradation, which directly affects the timeliness of bearing degradation prediction. Aiming at the difficulty of detecting the onset of degradation in the slow-changing degradation process of rolling bearings, this paper proposes a joint research framework of "onset of degradation detection-degradation prediction" for rolling bearings. Specifically, a health indicator construction method is proposed, and the detection of the onset of degradation of rolling bearings is realized. In addition, this indicator is very sensitive to the early failure of bearings. On this basis, a deep learning-based fault diagnosis and remaining useful life prediction (DFRP) model is established to realize the prediction of the degradation trend. We first divide the process of the vibration devices into common stage, degradation stage, and fault stage. The key features are extracted from the pre-processing signals and put into the prediction model together. Sufficient experimental results verify that under the condition of limited training samples, the proposed DFRP model obtains better prediction results compared with the Relevance Vector Machine (RVM) and Recurrent Neural Network (RNN) methods.