Multivariate Phase Space Warping-Based Degradation Tracking and Remaining Useful Life Prediction of Rolling Bearings

Effective utilization of signals collected by distributed sensor networks is crucial for tracking degradation and forecasting the remaining useful life (RUL) of rolling bearings. The phase space warping (PSW) algorithm constructs the hierarchical dynamics to physically describe damage evolution. However, the PSW algorithm is unable to handle multivariate signals. To enable synchronous tracking of degradation in multivariate signals, the proposed solution is the multivariate phase space warping (MPSW) algorithm. First, the multivariate signals are embedded in the reconstructed phase space. Second, the local polynomial receives the current phase space trajectory (PST) to predict the reference PST, after which damage indicators are extracted by comparing the current PST with the reference PST. Third, robust principal component analysis with tensor smooth constraint was proposed on the DIs tensor to extract the main degradation pattern. Finally, the degradation is input to the exponential degradation model to predict the RUL. The run-to-failure experimental datasets for rolling bearings are applied to validate the effectiveness of the proposed MPSW. Experimental results demonstrate that the proposed MPSW effectively tracks the multivariate degradation, and accurately predicts the RUL with distributed sensor networks.