Marine CM: Condition identification of the cylinder liner-piston ring in a marine diesel engine using bispectrum analysis and artificial neural networks
Diesel engines have been widely used in various engineering applications. The cylinder liner-piston ring (CLPR) is one of the most important rubbing pairs and its operating condition greatly influences the performance of diesel engines. It is, therefore, crucial to investigate and monitor
the working condition of the CLPR. Marine diesel engines are very complicated systems composed of a series of mechanical components. The vibration signal of the CLPR is strongly coupled with the vibration signatures of other components. It is imperative to inspect the coupling effect on identification
reliability in the process of CLPR condition identification. For this reason, a method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for monitoring the CLPR conditions, with consideration given to the impact of the other components in the marine diesel engine.
Bispectrum analysis was first employed to detect the running state of the cylinder liner-piston ring. The amplitude-frequency plots containing its characteristic signals were then obtained based on the bispectrum. Both the back-propagation neural network (BPNN) and the radial basis function
neural network (RBFNN) were applied to identify the state of the CLPR. The experimental test results show that the bispectral patterns of the pairs in various running states are different, so the distinct running state features of the vibrant signal can be extracted effectively using the bispectrum.
The ANN classification method has achieved a high detection accuracy. The proposed techniques of condition identification have the capability of identifying the running states of the CLPR in an early phase and can be applied in practice.
Keywords: ARTIFICIAL NEURAL NETWORKS; BISPECTRUM; CONDITION IDENTIFICATION; CYLINDER LINER-PISTON RING (CLPR); MARINE DIESEL ENGINE
Document Type: Research Article
Publication date: 01 November 2013
- Official Journal of The British Institute of Non-Destructive Testing - includes original research and development papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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