Abstract
A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.
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Foundation item: Supported by the National Natural Sciences Foundation of China (No. 50975213 and No. 50705070), Doctoral Fund for the New Teachers of Ministry of Education of China (No. 20070497029) and the Program of Introducing Talents of Discipline to Universities (No. B08031).
Zhixiong Li was born in 1983. He is a PhD candidate for vehicle application engineering at the School of Energy and Power Engineering of Wuhan University of Technology, China. His current research interests include condition monitoring and intelligent control system.
Xinping Yan was born in 1959. He is currently a professor at the School of Energy and Power Engineering, Wuhan University of Technology, China. He received his Ph.D. from Xi’an Jiaotong University, China, in 1997. His research interests include condition monitoring, fault diagnosis and application research of tribology.
Chengqing Yuan was born in 1976. He is currently a professor at the School of Energy and Power Engineering, Wuhan University of Technology, China. He received his PhD. from Wuhan University of Technology, China, in 2005. His research interests include tribology, condition monitoring and marine new energy.
Jiangbin Zhao was born in 1976. He is currently a faculty member of Wuhan University of Technology. He received PhD in mechanical engineering from Huazhong University of Science and Technology, China, in 2007. His research interests include automatic test and measurement system, condition monitoring and intelligent maintenance system.
Zhongxiao Peng was born in 1968. She is currently an associate professor in mechanical engineering at James Cook University, Australia. She received PhD in mechanical engineering from the University of Western Australia, Australia, in 2000. Her research interests include tribology, machine condition monitoring and fault diagnosis using wear debris and vibration analysis techniques, and wear analysis of bio-engineering systems.
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Li, Z., Yan, X., Yuan, C. et al. Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks. J. Marine. Sci. Appl. 10, 17–24 (2011). https://doi.org/10.1007/s11804-011-1036-7
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DOI: https://doi.org/10.1007/s11804-011-1036-7