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Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed

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Abstract

The normal operation of marine diesel engines ensures the scheduled completion and efficiency of a trip. Any failures may result in significant economic losses and severe accidents. It is therefore crucial to monitor the engine conditions in a reliable and timely manner in order to prevent the malfunctions of the plants. This work describes and evaluates the development and application of an intelligent diagnostic technique based on the integration of the empirical mode decomposition (EMD), kernel independent component analysis (KICA), Wigner bispectrum and support vector machine (SVM). It is an extension of the previous work on the fault detection for a diesel engine using the instantaneous angular speed (IAS). In this study, in order to solve the underdetermined blind source separation (BSS) problem the combination of EMD and KICA is firstly presented to estimate IAS signals from a single-channel IAS sensor. The KICA is also applied to select distinguished features extracted by Wigner bispectrum. The SVM is then employed for the multi-class recognition of the marine diesel engine faults in an intelligent way. Numerical simulations using a 6-cylinder engine model and real IAS data measured on the ship named “Hangjun 20” are used to evaluate the proposed method. Both the numerical and experimental diagnostic results have shown high efficiency of the proposed diagnostic method. Distinct fault features of the IAS signals have been extracted by the EMD-KICA and Wigner bispectrum, and the fault detection rate of the SVM is beyond 94.0%. Thus, the proposed method is feasible and available for the fault diagnosis of marine diesel engines.

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Authors and Affiliations

  1. Reliability Engineering Institute, School of Energy and Power Engineering, Key Laboratory of Marine Power Engineering & Technology, Ministry of Transportation, Wuhan University of Technology, Wuhan, 430063, China

    Zhixiong Li, Xinping Yan & Chengqing Yuan

  2. School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, 2052, Australia

    Zhongxiao Peng

Authors
  1. Zhixiong Li
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  2. Xinping Yan
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  3. Chengqing Yuan
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  4. Zhongxiao Peng
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Corresponding author

Correspondence to Xinping Yan.

Additional information

Recommended by Associate Editor Kyung-Soo Kim

Zhixiong Li is currently a Ph.D 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 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. During Nov. 1997–Jan. 1998, he was invited by the Royal Society as a visiting professor in University of Swansea, UK. He is a member of the editorial committee of Journal of Condition Monitoring and Diagnostic Engineering Management and Proceedings of the Institution of Mechanical Engineers — Part M: Journal of Engineering for the Maritime Environment. His research interests include condition monitoring, fault diagnosis and application research of tribology.

Zhongxiao Peng is currently an associate professor in School of Mechanical and Manufacturing Engineering at The University of New South Wales, Australia. She received her Ph.D in mechanical engineering from the University of Western Australia, Australia, in 2000. During Jan 2008 to May 2011, she was with School of Mechanical Engineering at James Cook University, Australia, as an associate professor. Her research interests include 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. Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed. J Mech Sci Technol 26, 2413–2423 (2012). https://doi.org/10.1007/s12206-012-0621-2

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  • DOI: https://doi.org/10.1007/s12206-012-0621-2

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