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Smart Structures and Systems Volume 13, Number 3, March 2014 , pages 453-471 DOI: https://doi.org/10.12989/sss.2014.13.3.453 |
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Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines |
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Changqing Shen, Dong Wang, Yongbin Liu, Fanrang Kong and Peter W. Tse
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| Abstract | ||
| The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods. | ||
| Key Words | ||
| statistical parameters; bearing fault diagnosis; deterioration evaluation; a two-layer structure; support vector regression machine | ||
| Address | ||
| Changqing Shen : Department of Systems Engineering & Engineering Management, City University of Hong Kong, China; School of Engineering Science, University of Science and Technology of China, Hefei 230026, China Dong Wang and Peter W. Tse : Department of Systems Engineering & Engineering Management, City University of Hong Kong, China Yongbin Liu and Fanrang Kong: School of Engineering Science, University of Science and Technology of China, Hefei 230026, China | ||