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).


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems


    Contributors:


    Publication date :

    2004


    Size :

    12 Seiten, 24 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





    Neural-fuzzy fault diagnosis in a marine propulsion shaft system

    Kuo, H.C. / Wu, L.J. / Chen, J.H. | Tema Archive | 2002


    Fault detection and diagnosis in propulsion systems - A fault parameter estimation approach

    Duyar, Ahmet / Eldem, Vasfi / Merrill, Walter et al. | AIAA | 1994



    Real-time fault diagnosis for propulsion systems

    Merrill, Walter C. / Guo, Ten-Huei / Delaat, John C. et al. | NTRS | 1991