A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution

Qing Xiong,Weihua Zhang,Shulin Liang,Guiming Mei,Tianwei Lu

doi:10.1155/2016/1232893

Qing Xiong, Weihua Zhang + Show 3 more

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https://doi.org/10.1155/2016/1232893

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Abstract

When rolling bearings fail, it is usually difficult to determine the degree of damage. To address this problem, a new fault diagnosis method was developed to perform feature extraction and intelligent classification of various fault position and damage degree of rolling bearing signals. Firstly, Multifractal Detrended Fluctuation Analysis (MFDFA) was used to compute five MFDFA features while five Alpha Stable Distribution (ASD) features were obtained by fitting the distribution to the vibration signals of each status and calculating the Probability Density Function (PDF). Secondly, Kernel Principle Component Analysis (KPCA) was used to achieve dimensionality reduction fusion of the combination of original features to gain the Kernel Principle Component Fusion Features (KPCFFs). Thirdly, the KPCFFs served as the input of Least Squares Support Vectors Machine (LSSVM) based on Particle Swarm Optimization (PSO) to assess rolling bearings’ fault position and damage severity. Finally, the effectiveness of the method was validated by bench test data. The results demonstrated that the developed method can achieve intelligent diagnosis of rolling bearings’ fault position and damage degree and can yield better diagnosis accuracy than single feature method or corresponding single feature fusion method.

Highlights

Rolling bearings are the key components of rotating machinery and their operational status directly influences the performance of the whole machine [1]
This paper introduces a new fault diagnosis method to achieve feature extraction and intelligent classification of different fault position and damage degree of rolling bearing signals based on feature fusion of Multifractal Detrended Fluctuation Analysis (MFDFA) and Alpha Stable Distribution (ASD)
Rauber et al in [6] used various feature models which are based on the timedomain and frequency-domain parameters, complex envelope spectrum, and wavelet packet analysis, utilized support vectors machine (SVM), the k-nearest neighbor classifier (k-NN), and multilayer perceptron (MLP) for classification of seven failures, and reported an accuracy of 98.13%, 99.96%, and 99.97%, respectively

Summary

Introduction

Rolling bearings are the key components of rotating machinery and their operational status directly influences the performance of the whole machine [1]. Critical work environment, such as high speed, heavy load, and repeated action of contact stress, makes it easy to deteriorate the operationalstatus of rolling bearings. The performance degradation of rolling bearings is a development process from minor faults to serious faults. Bourdon et al in [2] studied deeply the correlation between the length of the defect and Instantaneous Angular Speed (IAS) variations and proposed a signal processing tool to reconstruct the IAS variations caused by the damage of rolling bearings. Rauber et al in [6] and Sharma et al in [7] considered the time-domain and frequency-domain indexes as the fault features, studied the changes of these indexes under various defect severities, and obtained good results

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