In this paper, wavelets as signal processing tools are used to analyze the acceleration data acquired at the cylinder head for the detection and characterization of combustion malfunctions in multi-cylinder industrial engines. The objectives were to collect data on 1) normal operations, and 2) operations with a deactivated cylinder to simulate a faulty condition. Wavelet packet and local discriminatory basis algorithm are used to select wavelets that can recognize different conditions. It is shown that the wavelet packet provides a useful data analysis structure for extracting features that are capable of detecting the combustion malfunction of one cylinder in a 12-cylinder engine. Feature extraction is followed by a classification that uses a neural network for the fault identification phase.


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    Title :

    Malfunction Detection in Multi-cylinder Engines Using Wavelet Packet Dictionary


    Additional title:

    Sae Technical Papers


    Contributors:
    Sassani, F. (author) / Dumont, G. (author) / Ahmadi, H. (author) / Tafreshi, R. (author)

    Conference:

    SAE 2005 Noise and Vibration Conference and Exhibition ; 2005



    Publication date :

    2005-05-16




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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