The back propagation neural network (BPNN) is applied to the fault diagnosis for the propulsive system of a ship in this papaer. There exists a typical vibration signal that can be obtained as a training pattern for each type of fault. These typical vibration signals are collected as training data for the neural network to train the weights of the neural network. The input of the neural network is the characteristic parameters from the power spectrum density (PSD) of the vibration-acceleration signals measured in the shaft bearing system of a ship. By applying a combination of BPNN and fuzzy algorithm methods, an optimal propagation neural network and an optimal learning coefficient for the neural network are obtained. When faults occur, the vibration signals are collected as input of the BPNN. The BPNN will recall and judge what type of fault they belong to. Four testing types, normal condition, loose bearing, unbalanced propeller and loose base were chosen during the experiments. Forty-eight samples were taken in the range 100 to 500 rpm of the propeller rotational speed. The diagnosis results indicate that a correctness rate of 81.25 % on average can be reached and that the best result is a correctness rate of 91.7 % in the case of a loose bearing. The fault diagnosis model proposed in this paper can be applied only where each of the four faults occurs in isolation. If a situation arises where two ore more of these faults exist at the same time, or where different types of fault than those considered exist, this project would recommend a different method of characteristic collection.


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    Titel :

    Neural-fuzzy fault diagnosis in a marine propulsion shaft system


    Weitere Titelangaben:

    Neuronalnetz-Fuzzy-Fehlerdiagnose in einem Schiffsantriebswellensystem


    Beteiligte:
    Kuo, H.C. (Autor:in) / Wu, L.J. (Autor:in) / Chen, J.H. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2002


    Format / Umfang :

    11 Seiten, 10 Bilder, 2 Tabellen, 9 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch






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