Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network |
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| Authors: | Jian-Da Wu Jian-Ji Chan |
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| Affiliation: | 1. Departamento de Matemática e Estatística, Universidade Estadual de Ponta Grossa, 84030-900 Ponta Grossa-PR, Brazil;2. Departamento de Física, Universidade Federal do Maranhão, Campus Universitário do Bacanga, 65085-580 São Luís-MA, Brazil;1. Bogolyubov Institute for Theoretical Physics, National Academy of Sciences, 14-b Metrologichna Str., Kyiv, 03680, Ukraine;2. Physics Department, Taras Shevchenko National University of Kyiv, 64 Volodymyrska str., Kyiv, 01601, Ukraine;1. Department of Management and Engineering, University of Padova, Vicenza, Italy;2. Fatigue and Fracture Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Narmak, 16846 Tehran, Iran;1. Laboratoire Vibrations Acoustique, INSA-Lyon, 25 bis Avenue Jean Capelle, F-69621 Villeurbanne, Cedex, France;2. Katholieke Universiteit Leuven, Department of Mechanical Engineering, Box 2420, Celestijnenlaan 300 B, B-3001 Leuven, Belgium;3. Delft University of Technology, Faculty of Aerospace Engineering, Kluyverweg 1, 2629 HS Delft, The Netherlands;1. University of Padova, Department of Management and Engineering, Stradella S. Nicola 3, 36100 Vicenza, Italy;2. Department of Materials Science, Universidad Politécnica de Madrid E.T.S. Ingegneros de Caminos, 28040 Madrid, Spain |
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| Abstract: | In this paper, a condition monitoring and faults identification technique for rotating machineries using wavelet transform and artificial neural network is described. Most of the conventional techniques for condition monitoring and fault diagnosis in rotating machinery are based chiefly on analyzing the difference of vibration signal amplitude in the time domain or frequency spectrum. Unfortunately, in some applications, the vibration signal may not be available and the performance is limited. However, the sound emission signal serves as a promising alternative to the fault diagnosis system. In the present study, the sound emission of gear-set is used to evaluate the proposed fault diagnosis technique. In the experimental work, a continuous wavelet transform technique combined with a feature selection of energy spectrum is proposed for analyzing fault signals in a gear-set platform. The artificial neural network techniques both using probability neural network and conventional back-propagation network are compared in the system. The experimental results pointed out the sound emission can be used to monitor the condition of the gear-set platform and the proposed system achieved a fault recognition rate of 98% in the experimental gear-set platform. |
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