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Lookup NU author(s): Professor Ehsan Mesbahi
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This thesis demonstrates various novel applications of Artificial Neural Networks (ANNs) in the field of marine engineering concerning with sensor validation, fault diagnosis and the modelling and adaptive control of diesel engines. A biological resemblance between natural and artificial neural networks is identifed and studied. Inspired by the intensity thresholds and sensitivity characteristics of the human’s sensory mechanism, a novel non-linear normalistion technique is proposed for the recognition of a highly non-linear engineering application. This technique paves the way to a faster learning and better generalisation for ANNs. An integrated is proposed for on-line sensor validation and intelligent engine fault diagnostic system using auto-associative and standard ANNs. The proposed method utilises the data recovery capability of the auto-associative ANNs to arrive at a highly reliable platform for condition monitoring of diesel engines. The application of the method is successfully implemented on a Ruston medium speed diesel engine as a case study. An alternative technique for system identification utilising ANNs is introduced and applied on a time-variant dynamic system such as a high speed diesel engine. It is demonstrated that a global and dynamic model of the diesel engine at varying operational conditions can be established successfully by using recurrent ANNs in contrast to the conventional system identification techniques. For the applications in the area of intelligent control, the structure of a Model Reference Adaptive Controller (MRNAC) is used for the control of the engine speed. The inverse model of the identified engine, is successfully utilised for the training of the controller ANN. Finally, based on the successful application of MRNAC, a novel Neuro-Governor is designed and implemented to govern a high speed Perkins diesel engine.
Author(s): Mesbahi E
Publication type: Report
Publication status: Published
Series Title: Marine Technology
Year: 2000
Pages: 197
Institution: Newcastle University
Place Published: Newcastle upon Tyne
URL: http://www.ncl.ac.uk/marine/staff/profile/ehsan.mesbahi
