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              Citation: Fei Shen, Chao Chen, Jiawen Xu and Ruqiang Yan. A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions. Chinese Journal of Mechanical Engineering. doi: 10.1186/s10033-020-00437-3 shu

              A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

              • Author Bio: Fei Shen received his B.Sc. and M.Sc. degree from Southeast University, China, in 2014 and 2016 respectively. Now he is pursuing his PhD degree at School of Instrument Science and Engineering, Southeast University, China. His main research interest is machine fault diagnosis
                Chao Chen received his B.Sc. and M.Sc. degree from Jiangsu University, China, in 2011 and 2014 respectively. Now he is pursuing his PhD degree at School of Instrument Science and Engineering, Southeast University, China. His main research interest is machine fault diagnosis
                Jiawen Xu is currently an associate researcher at School of Instrument Science and Engineering, Southeast University, China
                Ruqiang Yan received his B.Sc. and M.E. degree from University of Science and Technology of China in 1997 and 2002 respectively, and received his Ph.D. degree in 2007 from University of Massachusetts, USA. Now he is a professor and a Ph.D. supervisor at Xi'an Jiaotong University, China. His main research interests include machine condition monitoring and fault diagnosis, signal processing, and wireless sensor networks
              • Corresponding author: Ruqiang Yan, ruqiang@seu.edu.cn
              • Received Date: 2019-03-06
                Accepted Date: 2020-01-18
                Available Online: 2020-02-28

                Fund Project: National Natural Science Foundation of China 51575102Jiangsu Postgraduate Research Innovation Program KYCX18_0075

              Figures(11) / Tables(12)

              • Most gear fault diagnosis (GFD) approaches suffer from inefficiency when facing with multiple varying working conditions at the same time. In this paper, a non-negative matrix factorization (NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix, aiming to offer a fast multi-tasking solution. The short-time Fourier transform (STFT) is first used to obtain the time-frequency features from the gear vibration signal. Then, the optimal clustering numbers are estimated using the Bayesian information criterion (BIC) theory, which possesses the simultaneous assessment capability, compared with traditional validity indexes. Subsequently, the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks. Finally, the parameters involved in BIC and NMF algorithms are determined using the gradient ascent (GA) strategy in order to achieve reliable diagnostic results. The Spectra Quest's Drivetrain Dynamics Simulator gear data sets were analyzed to verify the effectiveness of the proposed approach.
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