An Effective Bearing Fault Diagnosis Technique via Local Robust Principal Component Analysis and Multi-Scale Permutation Entropy

Abstract

The acquired bearing fault signal usually reveals nonlinear and non-stationary nature. Moreover, in the actual environment, some other interference components and strong background noise are unavoidable, which lead to the fault feature signal being weak. Considering the above issues, an effective bearing fault diagnosis technique via local robust principal component analysis (LRPCA) and multi-scale permutation entropy (MSPE) was introduced in this paper. Robust principal component analysis (RPCA) has proven to be a powerful de-noising method, which can extract a low-dimensional submanifold structure representing signal feature from the signal trajectory matrix. However, RPCA can only handle single-component signal. Therefore, in order to suppress background noise, an improved RPCA method named LRPCA is proposed to decompose the signal into several single-components. Since MSPE can efficiently evaluate the dynamic complexity and randomness of the signals under different scales, the fault-related single-components can be identified according the MPSE characteristic of the signals. Thereafter, these identified components are combined into a one-dimensional signal to represent the fault feature component for further diagnosis. The numerical simulation experimentation and the analysis of bearing outer race fault data both verified the effectiveness of the proposed technique.

Multiscale Entropy Approaches and Their Applications

“…Ge et al proposed a bearing fault diagnosis technique using the local robust principal component analysis (to remove background noise: it decomposed the signal trajectory matrix into multiple low-rank matrices) and multiscale permutation entropy that identified the low-rank matrices corresponding to the bearing’s fault feature [ 23 ]. The latter matrices are then combined into a one-dimensional signal and represents the extracted fault feature component.…”

Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis

“…The common bearing local faults mainly occur in the inner race, rolling element and outer race [2]. Refer to [45][46][47], the vibration signal model of bearing fault feature can be expressed as the superposition of multiple impulse excitations: Frequency (Hz) This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.…”

“…The lab bearing-gears fault test rig is shown in Fig.16, which consists of a variable frequency AC motor controlled by an encoder, two couplings, a gearbox with two pair of meshing gears and a magnetic powder loader [46]. The fault bearing is installed on the input shaft away from the motor side shown as the position 3 in Fig.16 (b), which is a single raw tapered roller bearing with the model number of 32206.…”

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

“…Nonlinear and nonstationary signals can be decomposed into multiple intrinsic mode of the sampled signal, and PCA cannot well retain the real information of the original signal. Ge et al [28] employed multi-scale displacement entropy (MDE) and robust PCA for rolling bearing fault diagnosis, which can effectively locate and diagnose bearing faults. However, the feature components of the acquired signal are more complex than the analog signals, and the noise reduction performance requires improvement.…”