You are being redirected because this document is part of your ASTM Compass® subscription.
    This document is part of your ASTM Compass® subscription.

    If you are an ASTM Compass Subscriber and this document is part of your subscription, you can access it for free at ASTM Compass
    Volume 35, Issue 4 (April 2007)

    Fault Classification of Water Hydraulic System by Vibration Analysis with Support Vector Machine

    (Received 21 July 2006; accepted 23 January 2007)

    Published Online: 13 April 2007

    CODEN: JTEVAB

      Format Pages Price  
    PDF (613.11 KB) 8 $25   ADD TO CART

    Cite this document

    X Add email address send
    X
      .RIS For RefWorks, EndNote, ProCite, Reference Manager, Zoteo, and many others.   .DOCX For Microsoft Word



    Abstract

    This paper presents a new neural network approach to the fault diagnosis of a water hydraulic system based on the wavelet analysis of a vibration signal. A novel feature of this approach is that the vibration signals acquired from the water hydraulic motor are employed for analysis. Wavelet transform (WT) is first applied as a feature extraction technique to analyze the time-domain vibration signal. The performance of support vector machine (svm) is then investigated and compared with the conventional neural network. The results confirm the applicability of the proposed method for the fault detection in a modern water hydraulic system.

    Author Information:

    Chen, H. X.
    School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore,

    Chua, Patrick S.K.
    School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore,

    Lim, G. H.
    School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore,


    Stock #: JTE100734

    ISSN:0090-3973

    DOI: 10.1520/JTE100734

    Author
    Title Fault Classification of Water Hydraulic System by Vibration Analysis with Support Vector Machine
    Symposium ,
    Committee E07