Fault Diagnosis of Ball Bearings Using Soft Computing

This paper presents the condition monitoring and fault diagnosis of rolling element bearings using Support Vector Machines (SVM). The vibration response of healthy bearings and bearings with various component defects such as outer race, inner race, balls and their combination have been analyzed. From the obtained vibration spectrum, it is clearly seen that a discrete peak of excitation appeared for the specific defect of bearings. In this paper, various faults of the bearings has been simulated and classified. The process includes, data acquisition, feature extraction from time response and a knowledge based system to classify faults. Features defining feature vectors are formed using statistical techniques and are fed as input to the support vector machine (SVM) classifiers. Knowledge based system developed for classification can be used for automatic recognition of machinery faults based on feature vector.

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Fault diagnosis of rolling element bearing compound faults based on sparse no-negative matrix factorization-support vector data description

Journal of Vibration and Control ◽

10.1177/1077546316637979 ◽

2016 ◽

Vol 24 (2) ◽

pp. 272-282 ◽

Cited By ~ 4

Author(s):

Hongchao Wang

Keyword(s):

Fault Diagnosis ◽

Matrix Factorization ◽

Rolling Element Bearing ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Outer Race ◽

Rolling Element ◽

Sparse Coefficient ◽

Element Bearing

The bispectrum of rolling element bearing compound faults contains abundant fault characteristic information, and how to extract the fault feature effectively is a key problem. The fault diagnosis method of rolling element bearing compound faults based on Sparse No-Negative Matrix Factorization (SNMF)-Support Vector Data Description (SVDD) is proposed in the paper. The figure handling method SNMF is used firstly in fault feature extraction of the bispectrums of rolling element bearing different kinds of compound faults and the sparse coefficient matrices of the bispectrums are obtained. The sparse coefficient matrices are used as training and test input vectors of SVDD. At last, the three kinds of rolling element bearing compound faults (inner race outer race compound faults, outer race rolling element compound faults and inner race outer race rolling element compound faults) are classified correctly. In order to verify the advantages of the proposed method, the diagnosis results of the same three kinds of rolling element bearing compound faults based on No-Negative Matrix Factorization (NMF)-SVDD is used as comparison. The proposed method provides a new idea for fault diagnosis of rolling element bearing compound faults.

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Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2013.07.006 ◽

2013 ◽

Vol 41 (1-2) ◽

pp. 127-140 ◽

Cited By ~ 161

Author(s):

Xiaoyuan Zhang ◽

Jianzhong Zhou

Keyword(s):

Fault Diagnosis ◽

Support Vector Machines ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Support Vector ◽

Rolling Element Bearings ◽

Mode Decomposition ◽

Vector Machines ◽

Rolling Element

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Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.00236 ◽

2010 ◽

Vol 30 (1) ◽

pp. 236-239 ◽

Cited By ~ 1

Author(s):

Liang XU

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Support Vector Machines ◽

Chemical Process ◽

Least Square ◽

Support Vector ◽

Vector Machines ◽

Nonlinear Feature Extraction ◽

Nonlinear Feature

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Data-driven prognostic scheme for rolling-element bearings using a new health index and variants of least-square support vector machines

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2021.107853 ◽

2021 ◽

Vol 160 ◽

pp. 107853

Author(s):

M.M. Manjurul Islam ◽

Alexander E. Prosvirin ◽

Jong-Myon Kim

Keyword(s):

Support Vector Machines ◽

Least Square ◽

Data Driven ◽

Support Vector ◽

Rolling Element Bearings ◽

Health Index ◽

Vector Machines ◽

Rolling Element

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A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

Shock and Vibration ◽

10.1155/2014/825825 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 25

Author(s):

HungLinh Ao ◽

Junsheng Cheng ◽

Kenli Li ◽

Tung Khac Truong

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Roller Bearing ◽

Local Characteristic ◽

Support Vector ◽

Svm Classifier ◽

Outer Race ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1014.510 ◽

2014 ◽

Vol 1014 ◽

pp. 510-515 ◽

Cited By ~ 1

Author(s):

You Cai Xu ◽

Xin Shi Li ◽

Ran Tao ◽

Shu Guo ◽

Min Gou ◽

...

Keyword(s):

Fault Diagnosis ◽

Predictive Model ◽

Feature Vector ◽

Local Mean Decomposition ◽

Outer Race ◽

Model Based ◽

Gear Fault ◽

Local Mean ◽

Gear Fault Diagnosis ◽

Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

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