Journal of Marine Science and Application

, Volume 10, Issue 1, pp 17–24

Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks

  • Zhixiong Li
  • Xinping Yan
  • Chengqing Yuan
  • Jiangbin Zhao
  • Zhongxiao Peng
Article

DOI: 10.1007/s11804-011-1036-7

Cite this article as:
Li, Z., Yan, X., Yuan, C. et al. J. Marine. Sci. Appl. (2011) 10: 17. doi:10.1007/s11804-011-1036-7

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.

Keywords

marine propulsion systemfault diagnosisvibration analysisbispectrumartificial neural networks

Copyright information

© Harbin Engineering University and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Zhixiong Li
    • 1
    • 2
  • Xinping Yan
    • 1
    • 2
  • Chengqing Yuan
    • 1
    • 2
  • Jiangbin Zhao
    • 1
    • 2
  • Zhongxiao Peng
    • 3
  1. 1.Reliability Engineering Institute, School of Energy and Power EngineeringWuhan University of TechnologyWuhanChina
  2. 2.Key Lab. of Marine Power Eng. and Tech. (Ministry of Transport)Wuhan University of TechnologyWuhanChina
  3. 3.School of Engineering and Physical SciencesJames Cook UniversityTownsvilleAustralia