Combining Multi-Scale Wavelet Entropy and Kernelized Classification for Bearing Multi-Fault Diagnosis.

Nibaldo Rodriguez,Guillermo Cabrera-Guerrero,Pablo Alvarez,Lida Barba

doi:10.3390/e21020152

Abstract

Discriminative feature extraction and rolling element bearing failure diagnostics are very important to ensure the reliability of rotating machines. Therefore, in this paper, we propose multi-scale wavelet Shannon entropy as a discriminative fault feature to improve the diagnosis accuracy of bearing fault under variable work conditions. To compute the multi-scale wavelet entropy, we consider integrating stationary wavelet packet transform with both dispersion (SWPDE) and permutation (SWPPE) entropies. The multi-scale entropy features extracted by our proposed methods are then passed on to the kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. In the end, both the SWPDE–KELM and the SWPPE–KELM methods are evaluated on two bearing vibration signal databases. We compare these two feature extraction methods to a recently proposed method called stationary wavelet packet singular value entropy (SWPSVE). Based on our results, we can say that the diagnosis accuracy obtained by the SWPDE–KELM method is slightly better than the SWPPE–KELM method and they both significantly outperform the SWPSVE–KELM method.

Highlights

Diagnosis of failures of bearings is a key factor to improve both safety and reliability of rotating machinery, intensively used in industrial environments
While the empirical mode decomposition (EMD) method can self-adaptively decompose a signal into some intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal [5], the local mode decomposition (LMD) method self-adaptively decomposes a signal into a series of product functions (PFs), each of which is exactly a mono-component signal [2]
In a previous work [39], we proposed an extreme learning machine (ELM) classifier based on a combination of stationary wavelet transform (SWT) and singular value decomposition (SVD)

Summary

Introduction

Diagnosis of failures of bearings is a key factor to improve both safety and reliability of rotating machinery, intensively used in industrial environments. One notable feature of this function is that it can show features of hidden failures [6,7,8,9] Based on these time-frequency methods for signal decomposition, different entropy features have been used such as Wiener-Shannon’s entropy [10,11], energy entropy [12,13], wavelet energy entropy [14], samples entropy [15], multiscale entropy [16,17], permutation entropy (PE) [18,19,20,21], multi-scale permutation entropy [22,23], generalized composite multiscale permutation entropy [24], multi-scale fuzzy entropy [25], composite multi-scale fuzzy entropy [26], dispersion entropy (DE) [27], multiscale dispersion entropy [28], and improved multiscale

Objectives

Methods

Results

Conclusion