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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/26835


    Title: Application of cepstrum and neural network to bearing fault detection
    Authors: Hwang,YR;Jen,KK;Shen,YT
    Contributors: 機械工程研究所
    Keywords: ROLLER-BEARINGS;STATOR CURRENT;EMD METHOD;DIAGNOSIS
    Date: 2009
    Issue Date: 2010-06-29 18:01:21 (UTC+8)
    Publisher: 中央大學
    Abstract: This paper proposes an integrated system for motor bearing diagnosis that combines the cepstrum coefficient method for feature extraction from motor vibration signals and artificial neural network (ANN) models. We divide the motor vibration signal, obtain the corresponding cepstrum coefficients, and classify the motor systems through ANN models. Utilizing the proposed method, one can identify the characteristics hiding inside a vibration signal and classify the signal, as well as diagnose the abnormalities. To evaluate this method, several tests for the normal and abnormal conditions were performed in the laboratory. The results show the effectiveness of cepstrum and ANN in detecting the bearing condition. The proposed method successfully extracted the corresponding feature vectors, distinguished the difference, and classified bearing faults correctly.
    Relation: JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
    Appears in Collections:[機械工程研究所] 期刊論文

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