Application of an intelligent classification method to mechanical fault diagnosis |
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| Authors: | Yaguo Lei Zhengjia He Yanyang Zi |
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| Affiliation: | 1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China;2. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China;1. State Key Laboratory for Manufacturing and Systems Engineering, Xi?an Jiaotong University, Xi?an 710049, China;2. State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi?an Jiaotong University, Xi?an 710049, China;1. Harbin Institute of Technology, Harbin, China;2. Bohai University, Jinzhou, China;1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China;2. School of Mechatronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, PR China;3. Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta T6G2G8, Canada;1. State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xi''an Jiaotong University, Xi''an 710049, PR China;2. Beijing Institute of Astronautical Systems Engineering, Beijing 100076, PR China |
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| Abstract: | A new method for intelligent fault diagnosis of rotating machinery based on wavelet packet transform (WPT), empirical mode decomposition (EMD), dimensionless parameters, a distance evaluation technique and radial basis function (RBF) network is proposed in this paper. In this method, WPT and EMD are, respectively, used to preprocess vibration signals to mine fault characteristic information more accurately. Then, dimensionless parameters in time domain are extracted from each of the original vibration signals and preprocessed signals to form a combined feature set. Moreover, the distance evaluation technique is utilised to calculate evaluation factors of the combined feature set. Finally, according to the evaluation factors, the corresponding sensitive features are selected and input into the RBF network to automatically identify different machine operation conditions. An experiment of rolling element bearings is carried out to test the performance of the proposed method. The experimental result demonstrates that the method combining WPT, EMD, the distance evaluation technique and the RBF network may accurately extract fault information and select sensitive features, and therefore it may correctly diagnose the different fault categories occurring in the bearings. Furthermore, this method is applied to slight rub fault diagnosis of a heavy oil catalytic cracking unit, the actual result shows the method may be applied to fault diagnosis of rotating machinery effectively. |
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