Fault diagnosis based on pulse coupled neural network and probability neural network |
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| Authors: | Changqing Wang Jianzhong Zhou Hui Qin Chaoshun Li Yongchuan Zhang |
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| Affiliation: | 1. College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China;2. The UAV T&R Section, Artillery Academy of PLA, Hefei 230031, PR China;1. China University of Mining and Technology, Xuzhou 221116, China;2. Langfang Branch of PetroChina Research Institute of Petroleum Exploration & Development, Langfang 065007, China;3. National Energy Shale Gas R&D (Experiment) Center, Langfang 065007, China;4. CNPC Unconventional Oil and Gas Laboratory, Langfang 065007, China;5. PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China;1. Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, PR China;2. University of Chinese Academy of Sciences, Beijing 100049, PR China;1. INSA Centre Val de Loire, Univ. Orléans, PRISME EA 4229, 88 boulevard Lahitolle, F-18022 Bourges, France;2. IUT de Bourges, Univ. Orléans, PRISME EA 4229, 63 Avenue de Lattre de Tassigny, F-18020 Bourges, France;3. Clinique Medipole Garonne, 45 rue de Gironis CS 13624, F-31036 Toulouse, France;1. Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China;2. Key Laboratory of Western Mineral Resources and Geological Engineering of Ministry of Education, Chang’an University, Xi’an 710054, China;3. Institutes of Geological Survey, China University of Geosciences, Wuhan 430074, China;4. Key Laboratory for the Study of Focused Magmatism and Giant Ore Deposits, Ministry of Land and Resources, Xi’an Center of Geological Survey, Xi’an 710054, Shanxi, China;1. Department of Architecture and Civil Engineering, College of Science and Engineering, City University of Hong Kong, Hong Kong, China;2. Division of Building Science and Technology, College of Science and Engineering, City University of Hong Kong, Hong Kong, China |
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| Abstract: | In operation of mechanical equipment, fault diagnosis plays an important role. In this paper, a novel fault diagnosis method based on pulse coupled neural network (PCNN) and probability neural network (PNN) is presented. The shape information of shaft orbit provides an important basis for fault diagnosis. However, the feature extraction and classification of shaft orbit is difficult to realize automation. The PCNN technique has excellent performance in the feature extraction. In the present study, a PCNN combined with roundness method is used to extract the feature vector of shaft orbit, because time signature from a PCNN has the property of insensitive to rotation, scaling and translation. Meanwhile, roundness is also with the same properties. Further, the PNN is used to train the feature vectors and classify the vibration fault. By comparison with the back-propagation (BP) network and radial-basic function (RBF) network, the experimental result indicated the proposed approach achieved fast and efficient fault diagnosis. |
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