A hybrid prognostic approach for extra-large-scale bearing using enhanced CERLMDAN-KPCA and MLKELM-AE

Abstract

Fault prognostics and health management (PHM) is essential for ensuring the high reliability and extending the lifespan of extra-large-scale bearings. Within the PHM of extra-large-scale bearing, signal de-noising is the top priority due to the presence of weak fault characteristic, which is almost submerged by the strong background noise. Under the premise of accurate signal de-noising, the next critical aspect of PHM depends on remaining useful life (RUL) prediction, which provides guidance for the operation and maintenance of extra-large-scale bearing. In view of these two aspects, a new signal de-noising method is proposed through the combination of complete ensemble robust local mean decomposition with adaptive noise with kernel principle component analysis (CERLMDAN-KPCA). Subsequently, the implementation of RUL prediction is conducted using multi-layer kernel extreme learning machine based auto-encoder (MLKELM-AE). During the processes of signal de-noising and RUL prediction, parameter optimization is carried out to enhance signal decomposition ability and prediction performance. Experimental results demonstrate that MLKELM-AE, combined with CERLMDAN-KPCA-based signal de-noising, achieves superior RUL prediction accuracy for extra-large-scale bearing.