Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dicti...

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Bibliographic Details
Published in:Machines (Basel) 2021-05, Vol.9 (5), p.98
Main Authors: Yuan, Haodong, Wu, Nailong, Chen, Xinyuan, Wang, Yueying
Format: Article
Language:English
Subjects:
Algorithms
Bearings
Dictionaries
dictionary learning
Fault diagnosis
Feature extraction
Invariants
Machine learning
Machinery
Neural networks
Optimization
Particle swarm optimization
Pattern recognition
Roller bearings
rolling bearing
Rotating machinery
shift invariant K-SVD
Signal processing
Singular value decomposition
support vector machine
Support vector machines
Teaching methods
Training
Vibration
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Summary:The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines9050098