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A New Symbiotic Evolution-based Fuzzy-neural Approach to Fault Diagnosis of Marine Propulsion Systems by Hsing-Chia Kuo; Hui-Kuo Chang is a Computer Science article available to read on EtoBox.

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This paper presents a symbiotic evolution-based fuzzy-neural diagnostic system (SE-FNDS) for fault diagnosis of propeller-shaft marine propulsion systems. The SE-FNDS combination of fuzzy modeling, back-propagation training and symbiotic evolution function auto-generates its own optimal fuzzy-neural architecture, a significant advantage over previous time-consuming manual parameter determination. Four hundred samples from a test propeller-shaft system are taken over a range of 100-500 rpm, during normal and experimentally induced faulty operation. This database is applied as input/output rule generation and training data for the fuzzy-neural network. Comparison of system construction time and diagnostic accuracy is made by applying the same database to SE-FNDS and four traditional systems. Compared to traditional methods, diagnostic decisions from SE-FNDS show 94.17% agreement with real conditions and less CPU time for system construction. Two nonlinear function approximations are also used to demonstrate the proposed system. The presented design is useful as a core module for more advanced computer-assisted diagnostic systems and for direct application in marine propulsion systems

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Author

Hsing-Chia Kuo; Hui-Kuo Chang

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0952-1976)

Published

2004

Language

EN

Field

Computer Science (Physical Sciences)