A novel intelligent method for inter-shaft bearing-fault diagnosis based on hierarchical permutation entropy and LLE-RF

Abstract

Since the transmission path of inter-shaft bearing-fault signal is complex, a fault feature extraction method based on hierarchical permutation entropy (HPE) and locally linear embedding (LLE) algorithm is proposed in this paper. In this method, HPE is utilized to extract fault information of signals, and LLE is utilized to reduce and fuse high-dimensional fault features of multi-sensors to construct fault samples. Then, the random forest (RF) model is established to diagnose the faults of the inter-shaft bearings. The fault simulation test rig with the inter-shaft bearing is built to simulate the normal bearing, inner ring fault, outer ring fault, and rolling ball fault, and the data are collected to verify the HPE-LLE-RF fault diagnosis algorithm of inter-shaft bearings established in this paper. The experimental results show that the proposed algorithm can extract the fault features of inter-shaft bearings effectively with a fault diagnosis accuracy of 93.3% without overfit phenomenon.