scholarly journals A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

2018 ◽  
Vol 8 (9) ◽  
pp. 1621 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Yong Ren ◽  
Gongbo Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Feature Space ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Acceleration Sensors ◽  
Reconstructed Signal ◽  
Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

Fault Diagnostics Based on Pattern Spectrum Entropy and Proximal Support Vector Machine

2009 ◽  
Vol 413-414 ◽  
pp. 607-612 ◽  
Author(s):  
Xiang Tao Yu ◽  
Wen Xiu Lu ◽  
Fu Lei Chu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Support Vector ◽  
Vibration Signals ◽  
Pattern Spectrum ◽  
Ring Fault ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Proximal Support Vector Machine

Based on pattern spectrum entropy and proximal support vector machine (PSVM), a motor rolling bearing fault diagnosis method is proposed in this paper. It is very difficult to filter the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification of inner ring fault, outer ring fault and ball fault. Firstly, vibration signals are processed by the pattern spectrum. Secondly, the morphological pattern spectrum entropy, and pattern spectrum values are utilized to identify the fault features of input parameters of PSVM classifiers. The experiment results demonstrate that the pattern spectrum quantifies various aspects of the shape-size content of a signal, and PSVM costs a little time and has better efficiency than the standard SVM.

scholarly journals Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals

2015 ◽  
Vol 10 (4) ◽  
pp. 1558-1565 ◽  
Author(s):  
Don-Ha Hwang ◽  
Young-Woo Youn ◽  
Jong-Ho Sun ◽  
Kyeong-Ho Choi ◽  
Jong-Ho Lee ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Correlation Coefficient of Simplified Neutrosophic Sets for Bearing Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lilian Shi
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Wavelet Packet ◽  
Energy Eigenvalue ◽  
Rolling Bearing ◽  
Neutrosophic Sets ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features

In order to process the vagueness in vibration fault diagnosis of rolling bearing, a new correlation coefficient of simplified neutrosophic sets (SNSs) is proposed. Vibration signals of rolling bearings are acquired by an acceleration sensor, and a morphological filter is used to reduce the noise effect. Wavelet packet is applied to decompose the vibration signals into eight subfrequency bands, and the eigenvectors associated with energy eigenvalue of each frequency are extracted for fault features. The SNSs of each fault types are established according to energy eigenvectors. Finally, a correlation coefficient of two SNSs is proposed to diagnose the bearing fault types. The experimental results show that the proposed method can effectively diagnose the bearing faults.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
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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