An adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis

Abstract

In this paper, a novel scheme for detecting bearing defects is proposed utilizing single-valued neutrosophic cross-entropy (SVNCE). Initially, the artificial hummingbird algorithm (AHA) is used to make the feature mode decomposition (FMD) adaptive by optimizing its parameter based on a novel health indicator (HI) i.e. sparsity impact measure index (SIMI). This HI ensures full sparsity and impact properties simultaneously. The raw signals are decomposed into different modes by adaptive FMD at optimal values of its parameters. The energy of these modes is calculated for different health conditions. The energy interval range has been decided based on energy eigen which are then transformed into single-valued neutrosophic sets (SVNSs) for unknown defect conditions. The minimum argument principle employs the least SVNCE values between SVNSs of testing samples and SVNSs of training samples to recognize the different defects in the bearing. The proposed methodology is applied to bearing from industrial mechanical systems.