Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

Shuen-De Wu,Po-Hung Wu,Chun-Chieh Wang,Chiu-Wen Wu,Jian-Jiun Ding

doi:10.3390/e14081343

Shuen-De Wu, Po-Hung Wu + Show 3 more

Open Access

https://doi.org/10.3390/e14081343

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Abstract

Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE) and multiscale entropy (MSE).

Highlights

Bearings are the most frequently used component in a rotary machine
In order to validate the capability of the mutliscale permutation entropy (MPE) algorithm, experimental analyses on bearing faults were conducted
Multiscale permutation entropy (MPE) is an effective way to measure the complexity of chaotic time series, such as the vibration signal of bearings in our experiments

Summary

Introduction

Bearings are the most frequently used component in a rotary machine. Bearing failures could lead to unpredictable productivity losses for production facilities. Vibration-based signal analysis in the time-frequency domain has been a major technique for bearing fault diagnosis. Time-frequency analysis methods, such as the short-time Fourier transform [3], the Wigner Ville distribution [4], and the wavelet transform [5], have been widely used to detect bearing faults since they can provide abundant information about machine faults. Both ApEn and MSE can be used for measuring the regularity of a time series These entropy-based methods are simple and require much less computation time, they have very good performance in bearing fault diagnosis. Based on a single scale algorithm, the PE based method has limited performance in analyzing these complicated data To overcome this shortcoming, based on the concept of multiscale [16], Aziz proposed a new method termed mutliscale permutation entropy (MPE) to calculate entropy over multiple scales [17].

Permutation Entropy

Experimental Data

Results

Conclusions