A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network |
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| Authors: | Jian-Da Wu Yu-Hsuan Wang Peng-Hsin Chiang Mingsian R Bai |
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| Affiliation: | 1. Graduate Institute of Vehicle Engineering, National Changhua University of Education, 1 Jin-De Rd., Changhua City, Changhua 500, Taiwan;2. Department of Mechanical Engineering, National Chiao-Tung University, Hsin-Chu, Taiwan;1. State Key Laboratory for Manufacturing Systems Engineering, Xi?an Jiaotong University, Xi?an 710049, PR China;2. Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, Chang?an University, Xi?an 710064, PR China;3. Air Force Engineering University, Xi?an 710038, PR China;1. Liaoning Key Laboratory of Advanced Test Technology for Aeronautical Propulsion System, Shenyang Aerospace University, Shenyang 110136, PR China;2. Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, PR China;3. School of Energy and Power Engineering, Beihang University, Beijing 100191, PR China;1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Equipment Reliability, Prognostics and Health Management Lab (ERPHM), China;2. Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, T6G 2G8, Canada |
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| Abstract: | An expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and neural networks is presented in this paper. The order tracking technique is one of the important approaches for fault diagnosis in rotating machinery. The different faults present different order figures and they can be used to determine the fault in mechanical systems. However, many breakdowns are hard to classify correctly by human experience in fault diagnosis. In the present study, the order tracking problem is treated as a parametric identification and the artificial neural network technique for classifying faults. First, the adaptive order tracking extract the order features as input for neural network in the proposed system. The neural networks are used to develop the training module and testing module. The artificial neural network techniques using a back-propagation network and a radial basis function network are proposed to develop the artificial neural network for fault diagnosis system. The performance of two techniques are evaluated and compared through experimental investigation. The experimental results indicated that the proposed system is effective for fault diagnosis under various engine conditions. |
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