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    Accelerating 2d fault diagnosis of an induction motor using a graphics processing unit

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    Date
    2015
    Publisher
    © 2015 Science and Engineering Research Support Society
    Author
    Uddin, Jia
    Van, Dinh Nguyen
    Kim, Jong-Myon
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10361/7004
    Citation
    Uddin, J., Nguyen Van, D., & Kim, J. -. (2015). Accelerating 2d fault diagnosis of an induction motor using a graphics processing unit. International Journal of Multimedia and Ubiquitous Engineering, 10(1), 341-352. doi:10.14257/ijmue.2015.10.1.32
    Abstract
    This paper presents a computationally efficient graphics processing unit (GPU) implementation of a reliable fault diagnosis method using two-dimensional (2D) representation of vibration signals. The fault diagnosis method first converts time-domain vibration signals into 2D gray-level images to exploit texture information from the converted images. Then, the global dominant neighborhood structure (GNS) map is utilized to extract texture features by averaging local neighborhood structure (LNS) maps of central pixels. In addition, the principle component analysis (PCA) algorithm is employed to select only the most dominant features. Finally, the selected features are used as inputs to a one-against-all multi-class support vector machine (OAA-MCSVM) to identify each fault of the induction motor. Despite the fact that the 2D fault diagnosis methodology shows satisfactory classification accuracy, its computational complexity limits its use in real-time applications. To accelerate the 2D fault diagnosis method, this paper utilizes an NVIDIA GeForce GTX 580 GPU, where all tasks are executed in parallel. The experimental results indicate that the proposed GPU-based approach achieves about 118.5 faster operation than the equivalent sequential CPU implementation while maintaining 100% classification accuracy.
    Keywords
    Fault diagnosis; Global dominant neighborhood structure (GNS); Graphics processing unit; Induction motor; Local neighborhood structure (LNS)
     
    Description
    This article was published in the Journal of Applied Mathematics [© 2015 SERSC] and the definite version is available at :http://dx.doi.org/10.14257/ijmue.2015.10.1.32 The Journal's website is at:http://www.sersc.org/journals/IJMUE/vol10_no1_2015/32.pdf
    Publisher Link
    http://www.sersc.org/journals/IJMUE/vol10_no1_2015/32.pdf
    DOI
    :http://dx.doi.org/10.14257/ijmue.2015.10.1.32
    Department
    Department of Computer Science and Engineering, BRAC University
    Type
    Article
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