Support Vector Machines (SVMs) are being used extensively now days in the arena of pattern recognition and regression analysis. It has become a good choice for machine learning both for supervised and unsupervised learning purposes. The SVM is primarily based on the mapping the data to a hyperplane using some kernel function and then increasing the margin between the hype planes so this hyperplane classifies the data in the normal and fault state. Due to large amount of input data, it is computationally cumbersome to yield the desired results in shortest possible time by using SVM. To overcome this difficulty in this work, we have employed statistical Time-Domain Features like Root Mean Square (RMS), Variance, Skewness and Kurtosis as pre-processors to the input raw data. Then various combinations of these time-domains signals and features have been used as inputs and their effects on the optimal model selection have been investigated thoroughly and optimal one has been suggested. The procedure presented here is computational less expensive otherwise to process the input data for model selection we may have to use super computer. The implementation of proposed method for machine learning is not much complicated and by using this procedure, an impending fault/abnormal behavior of the machine can be detected beforehand.
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ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 3–6, 2008
Brooklyn, New York, USA
Conference Sponsors:
- Design Engineering Division and Computers in Engineering Division
ISBN:
978-0-7918-4327-7
PROCEEDINGS PAPER
Optimal Model Selection of Suport Vector Classifiers for Rolling Element Bearings Fault Detection Using Statistical Time-Domain Features
Z. Hameed,
Z. Hameed
Seoul National University, Seoul, South Korea
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Y. S. Hong,
Y. S. Hong
Seoul National University, Seoul, South Korea
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Y. M. Cho,
Y. M. Cho
Seoul National University, Seoul, South Korea
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S. H. Ahn,
S. H. Ahn
Seoul National University, Seoul, South Korea
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C. K. Song
C. K. Song
Gyeongsang National University, Jinju, Gyeongnam, South Korea
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Z. Hameed
Seoul National University, Seoul, South Korea
Y. S. Hong
Seoul National University, Seoul, South Korea
Y. M. Cho
Seoul National University, Seoul, South Korea
S. H. Ahn
Seoul National University, Seoul, South Korea
C. K. Song
Gyeongsang National University, Jinju, Gyeongnam, South Korea
Paper No:
DETC2008-49772, pp. 1359-1368; 10 pages
Published Online:
July 13, 2009
Citation
Hameed, Z, Hong, YS, Cho, YM, Ahn, SH, & Song, CK. "Optimal Model Selection of Suport Vector Classifiers for Rolling Element Bearings Fault Detection Using Statistical Time-Domain Features." Proceedings of the ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3: 28th Computers and Information in Engineering Conference, Parts A and B. Brooklyn, New York, USA. August 3–6, 2008. pp. 1359-1368. ASME. https://doi.org/10.1115/DETC2008-49772
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