Health monitoring of gears is very critical for satisfactorily overall working of the complex machinery. Thus, the ability to detect gear faults and classify them based on their nature becomes very important aspect of health monitoring of machines. In this paper, SVM algorithms have been used for the multiclass prediction of faults with the help of time domain vibration signals obtained from the gearbox casing operated in a suitable speed range. Moreover, it tries to examine the performance of the SVM technique by optimizing its parameters on utilization of time domain data from multi-fault gear box. The SVM software was fed with the training data and testing data at similar operating speeds for three types of defects and no defect case, and classification ability of SVM was noted and found to be excellent. The sensitivity analysis of optimized parameters is studied and conclusions are drawn.
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ASME 2012 Gas Turbine India Conference
December 1, 2012
Mumbai, Maharashtra, India
Conference Sponsors:
- International Gas Turbine Institute
ISBN:
978-0-7918-4516-5
PROCEEDINGS PAPER
Health Monitoring of Gears Based on Vibrations by Support Vector Machine Algorithms Available to Purchase
D. J. Bordoloi,
D. J. Bordoloi
Indian Institute of Technology Guwahati, Guwahati, India
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Rajiv Tiwari
Rajiv Tiwari
Indian Institute of Technology Guwahati, Guwahati, India
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D. J. Bordoloi
Indian Institute of Technology Guwahati, Guwahati, India
Rajiv Tiwari
Indian Institute of Technology Guwahati, Guwahati, India
Paper No:
GTINDIA2012-9586, pp. 639-648; 10 pages
Published Online:
July 25, 2013
Citation
Bordoloi, DJ, & Tiwari, R. "Health Monitoring of Gears Based on Vibrations by Support Vector Machine Algorithms." Proceedings of the ASME 2012 Gas Turbine India Conference. ASME 2012 Gas Turbine India Conference. Mumbai, Maharashtra, India. December 1, 2012. pp. 639-648. ASME. https://doi.org/10.1115/GTINDIA2012-9586
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