Abstract
A semi-supervised Laplacian Eigenmaps algorithm for machine fault detection is proposed. The purpose of the algorithm is to efficiently extract the manifold geometric characteristics of nonlinear vibration signal samples, and to determine fault classification of operating equipment so that the accuracy of fault detection can be improved. The data acquisition and pre-processing of the vibration signal is firstly implemented from monitoring equipment, then hybrid domain feature is obtained, and the initial sample set can be built. This is followed by implementing the semi-supervised Laplacian Eigenmaps algorithm so that the sensitive nature characteristics of manifold can be obtained from the device initial sample set. In order to establish the intelligent diagnostic model, the Least square Support vector machine (LS-SVM) is then adopted, which fault diagnosis and decisions can be achieved in the feature space of the low-dimensional manifold. The experiment results of using the IRIS data, gearbox and compressor fault data show the proposed method has more advantage when compared with the PCA and Laplacian Eigenmaps on improving the accuracy of fault detection.
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Recommended by Associate Editor Byeng Dong Youn
Quansheng Jiang received his Ph.D. degree in mechanical engineering from Southeast University, China, in 2009. Currently he is an Associate Professor at School of Mechanical Engineering, Suzhou University of Science and Technology, China. His research interests include signal processing and fault detection for mechanical systems.
Qixin Zhu received his Ph.D. degree in Control Theory and Control Engineering from Nanjing University of Aeronautics and Astronautics in 2003. Currently he is a Full Professor at School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou, China. His research interests include intelligent control, robot and the applications of control theory in engineering.
Bangfu Wang received his M.S. degree in mechanical engineering from Jiangsu University, China, in 2005. Currently he is a Lecturer at School of Mechanical Engineering, Suzhou University of Science and Technology, China. His research interests are Mechatronics and fault detection.
Lihua Guo received her Ph.D. degree in mechanical engineering from Southeast University, China, in 2011. Currently she is a Lecturer at School of Mechanical Engineering, Suzhou University of Science and Technology, China. Her research interests are in optimization algorithm for mechanical engineering.
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Jiang, Q., Zhu, Q., Wang, B. et al. Nonlinear machine fault detection by semi-supervised Laplacian Eigenmaps. J Mech Sci Technol 31, 3697–3703 (2017). https://doi.org/10.1007/s12206-017-0712-1
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DOI: https://doi.org/10.1007/s12206-017-0712-1