Remaining Useful Life Prediction of Hybrid Drive and CWT-CDC Deep-Coupled Rolling Bearing

Prognostics and Health Management integrated bearing remaining life prediction not only supports condition-based maintenance decision closed loop, moreover, the on-time task rate is improved by dynamic scheduling of maintenance resources, and the continuous improvement of equipment comprehensive efficiency is promoted. In this paper, a hybrid-driven Remaining Useful Life (RUL) evaluation way of bearing is constructed, which innovatively realizes the deep coupling of data-driven and mechanism model by fusing improved DenseNet and nonlinear least square degradation modeling. First of all, the time-domain statistical features of vibration signals are selected by using monotonicity and robustness, and the degradation trajectory index(DI) is established to determine the Fault Incipience Point (FIP). Secondly, a joint feature learning framework of Continuous Wavelet Transform and Causal Dilated Convolution (CWT-CDC) was constructed: the time-frequency map was generated by Morlet wavelet kernel function, and the local damage feature was enhanced by causal dilated convolution, and the nonlinear mapping from equipment Health Indicators (HI) to RUL value was realized. Finally, the superiority of the proposed appoach are verified by the XJTU-SY bearing data set.