False Lipschitz Penalty Sparse Low-Rank Matrix and Chaotic Bionic Optimization for Prognosis of Bearing Degradation

Abstract

Accurately predicting the degradation trend of a pivotal component is essential for scheduling maintenance decisions, avoiding abrupt shutdowns of mechanical equipment, and improving overall systems reliability. This article proposes a hybrid data-driven approach for bearing degradation prognosis that utilizes sparse low-rank matrix (SLRM) and chaotic bionic optimization algorithms based upon bearings health indicator time series (HITS). First, by mean of SLRM method, the HITS of bearings can be adaptively decomposed into low frequency trend component (LFC) and high frequency oscillation component (HFC), respectively. A novel non-convex regularizer named false Lipschitz penalty is designed, and the strict convexity of the proposed cost function can be ensured. Meanwhile, an efficient iterative algorithm using the forward–backward splitting technique is presented. Second, a metaheuristic algorithm named chaotic cuckoo search (CCS) is utilized to optimize the key parameters of the support vector machine (SVM) model, and the decomposed LFC and HFC are, respectively, predicted via the CCS-SVM approach. The final predicted time series is obtained after a summation operation. The proposed hybrid approach is evaluated using accelerated degradation test data of rolling bearings. Experimental results demonstrate the effectiveness and superiority of the proposed approach in comparison with five benchmark methods.