• Login
    • Library Home
    View Item 
    •   BracU IR
    • BracU Faculty Publications
    • Dr. Jia Uddin
    • Article
    • View Item
    •   BracU IR
    • BracU Faculty Publications
    • Dr. Jia Uddin
    • Article
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines

    Thumbnail
    Date
    2016
    Publisher
    © 2016 Old City Publishing, Inc.
    Author
    Islam, Md Rashedul
    Uddin, Jia
    Kim, Jongmyon
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10361/9576
    Citation
    Islam, M. R., Uddin, J., & Kim, J. -. (2016). Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines. Ad-Hoc and Sensor Wireless Networks, 34(1-4), 273-287.
    Abstract
    Reliable and efficient fault diagnosis of induction motors is an important issue in industrial environments. This paper proposes a method for reliable fault diagnosis of induction motors using signal processing of acoustic emission (AE) data, including Gabor filtering and the use of multiclass support vector machines (MCSVMs), where a ZigBee based wireless sensor network (WSN) model is used for efficiently transmitting AE signals to a diagnosis server. In the proposed fault diagnosis approach, the induction motor’s different state signals are acquired through proper placement of AE sensors. The AE data are sent to a server through the wireless sensor network and decomposed using discrete wavelet transformation (DWT). An appropriate band is then selected using the maximum energy ratio, and a one-dimensional (1D) Gabor filter with various frequencies and orientation angles is applied to reduce abnormalities and extract various statistical parameters for generating features. In addition, principal component analysis (PCA) is applied to the extracted features to select the most dominant feature dimensions. Finally, one-against-one multiclass support vector machines (OAA-MCSVMs) are used to classify multiple fault types of an induction motor, where each SVM individually trains with its own features to increase the fault classification accuracy of the induction motor. In experiments, the proposed approach achieved an average classification accuracy of 99.80%, outperforming conventional fault diagnosis models.
    Keywords
    Acoustic emission; Discrete wavelet transform; Fault diagnosis; Gabor filter; Induction motor; Wireless sensor network
     
    Description
    This article was published in the Ad-Hoc and Sensor Wireless Networks [© 2016 Old City Publishing, Inc.]
    Department
    Department of Computer Science and Engineering, BRAC University
    Type
    Article
    Collections
    • Article
    • Faculty Publications

    Copyright © 2008-2023 Ayesha Abed Library, Brac University 
    Contact Us | Send Feedback
     

     

    Policy Guidelines

    • BracU Policy
    • Publisher Policy

    Browse

    All of BracU Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Copyright © 2008-2023 Ayesha Abed Library, Brac University 
    Contact Us | Send Feedback