NDLI: Fault classification in gears using support vector machines (SVMs) and signal processing

Content Provider	IEEE Xplore Digital Library
Author	Soleimani, A. Mahjoob, M.J. Shariatpanahi, M.
Copyright Year	2009
Description	Author affiliation: Noise, Vibration, Acoustics (NVA) Research Center, School of Mechanical Engineering, University of Tehran, Iran (Soleimani, A.; Mahjoob, M.J.; Shariatpanahi, M.)
Abstract	This study presents a procedure for gear fault identification based on vibration signal processing techniques and support vector machines (SVMs). The required feature vector is extracted from vibration signals by time, frequency and time-frequency analysis. A feature selection technique based on Euclidian distance is utilized and five salient features are selected from the original feature set. These features are fed into the classification algorithm. Gear conditions considered were healthy, slightly worn, medium worn and broken-teeth gears. The output of classifier algorithm indicates the status of the gearbox by four labels. The results show that the developed SVM-based procedure is able to discriminate the faults clearly. The effectiveness of the feature selection method is demonstrated by experiments.
Starting Page	1
Ending Page	4
File Size	500315
Page Count	4
File Format	PDF
ISBN	9781424434299
DOI	10.1109/ICSCCW.2009.5379494
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-02
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Vibrations Gears Fault detection Support vector machine classification Signal processing algorithms Artificial neural networks Signal processing Feature extraction Signal analysis
Content Type	Text
Resource Type	Article

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