Abstract
The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.
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Recommended by Associate Editor Byeng Dong Youn
Jianfeng Qu received the B.E. degree, M.E. degree and the Ph.D. degree in College of Automation from Chongqing University, China. Prof. Qu joined the College of Automation at the Chongqing University in 2010. He is currently an Associate Professor the College of Automation at the Chongqing University, China. His research and teaching interests include signal detection and analysis, non-stationary signal analysis, datadriven modeling and analysis.
Zhanqiang Xing received the B.E. degree in College of Automation from Chongqing University, China. He is currently a master student in the College of Automation from Chongqing University. His research interests include fault diagnosis and signal processing.
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Xing, Z., Qu, J., Chai, Y. et al. Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine. J Mech Sci Technol 31, 545–553 (2017). https://doi.org/10.1007/s12206-017-0107-3
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DOI: https://doi.org/10.1007/s12206-017-0107-3