It is common to see difficult feature extraction in heavy-duty vehicles fault diagnosis due to strong interference. Blind signal separation(BSS) technology proves to be effective to extract the principal component out of the multi-sources signals. Therefore, it is used to extract the fault information for heavy-duty vehicle in this paper. A bispectrum of the data after BSS is obtained and scanned in frequency field. The result indicates that BSS can reduce the interference out of the engine vibration and extract the wanted fault features more effectively.


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    Title :

    Applying blind signal separation theory to diagnose heavy-duty vehicle


    Contributors:
    Huimin Zhao (author) / Jianmin Mei (author) / Hong Shen (author) / Qingle Yang (author)


    Publication date :

    2014-08-01


    Size :

    856735 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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