Fault diagnosis of rolling element bearings via discriminative subspace learning: Visualization and classification |
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| Affiliation: | 1. Department of Electronics Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region;2. Department of Computer Science and Technology, Soochow University, Suzhou 215006, China;1. Fraunhofer INT, Appelsgarten 2, D-53879 Euskirchen, Germany;2. Ghent University, Faculty of Economics and Business Administration, Tweekerkenstraat 2, B-9000 Gent, Belgium;1. Department of Information Management at Fortune Institute of Technology, Kaohsiung, Taiwan;2. Thecus Technology Corporation, Taiwan;3. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;1. Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;2. Graduate Telecommunications and Networking Program, University of Pittsburgh, PA, USA;3. China Internet Research Lab, China Science and Technology Network, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China;4. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China |
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| Abstract: | Rolling element bearings play an important role in ensuring the availability of industrial machines. Unexpected bearing failures in such machines during field operation can lead to machine breakdown, which may have some pretty severe implications. To address such concern, we extend our algorithm for solving trace ratio problem in linear discriminant analysis to diagnose faulty bearings in this paper. Our algorithm is validated by comparison with other state-of art methods based on a UCI data set, and then be extended to rolling element bearing data. Through the construction of feature data set from sensor-based vibration signals of bearing, the fault diagnosis problem is solved as a pattern classification and recognition way. The two-dimensional visualization and classification accuracy of bearing data show that our algorithm is able to recognize different bearing fault categories effectively. Thus, it can be considered as a promising method for fault diagnosis. |
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| Keywords: | Fault diagnosis Linear discriminant analysis Rolling element bearing Pattern recognition Vibrations |
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