Abstract
Time-frequency distribution of vibration signal can be considered as an image that contains more information than signal in time domain. Manifold learning is a novel theory for image recognition that can be also applied to rotating machinery fault pattern recognition based on time-frequency distributions. However, the vibration signal of rotating machinery in fault condition contains cyclical transient impulses with different phrases which are detrimental to image recognition for time-frequency distribution. To eliminate the effects of phase differences and extract the inherent features of time-frequency distributions, a multiscale singular value manifold method is proposed. The obtained low-dimensional multiscale singular value manifold features can reveal the differences of different fault patterns and they are applicable to classification and diagnosis. Experimental verification proves that the performance of the proposed method is superior in rotating machinery fault diagnosis.
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Recommended by Associate Editor Byeng Dong Youn
Yi Feng received his B.S. from Nanjing University of Science and Technology, Nanjing, China, in 2012. He is currently working toward the Ph.D. in Mechanical Engineering. His research interests include signal processing and fault diagnosis.
Baochun Lu received his Ph.D. from Nanjing University of Science and Technology, Nanjing, China, in 2002. He currently works at Nanjing University of Science and Technology. His research interests include mechanical engineering automation and fault diagnosis.
Dengfeng Zhang received his Ph.D. from Nanjing University of Science and Technology, Nanjing, China, in 2003. He currently works at Nanjing University of Science and Technology. His research interests include fault tolerant control.
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Feng, Y., Lu, B. & Zhang, D. Multiscale singular value manifold for rotating machinery fault diagnosis. J Mech Sci Technol 31, 99–109 (2017). https://doi.org/10.1007/s12206-016-1210-6
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DOI: https://doi.org/10.1007/s12206-016-1210-6