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 100-500rpm, 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. (All rights reserved Elsevier).
A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems
Engineering Applications of Artificial Intelligence ; 17 , 8 ; 919-930
2004
12 Seiten, 24 Quellen
Article (Journal)
English
Neural-fuzzy fault diagnosis in a marine propulsion shaft system
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