NDLI: Fault diagnosis for drivetrain gearboxes using PSO-optimized multiclass SVM classifier

Content Provider	IEEE Xplore Digital Library
Author	Dingguo Lu Wei Qiao
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Univ. of Nebraska-Lincoln, Lincoln, NE, USA (Dingguo Lu; Wei Qiao)
Abstract	A novel method consisting of an adaptive feature extraction scheme and a particle swarm optimization (PSO)-optimized multiclass support vector machine (SVM) classifier is proposed for condition monitoring and fault diagnosis of drivetrain gearboxes in variable-speed operational conditions. The adaptive feature extraction scheme consists of an adaptive signal resampling algorithm, a frequency tracker, and a feature generation algorithm for effective extraction of the features of gearbox faults from the stator current signal of the AC electric machine connected to the gearbox. The multiclass SVM classifier is designed to identify different faults in the gearbox according to the fault features extracted. The PSO algorithm is utilized to optimize the parameter setting of the SVM classifier to obtain the best classification accuracy. The proposed method is testified on a drivetrain gearbox connected with a permanent-magnet synchronous machine with three different faults. Experimental results show that the faults can be effectively classified by the proposed method.
Starting Page	1
Ending Page	5
File Size	564745
Page Count	5
File Format	PDF
ISBN	9781479964154
DOI	10.1109/PESGM.2014.6939892
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Feature extraction Classification algorithms Stators Shafts Algorithm design and analysis Amplitude modulation support vector machine (SVM) Condition monitoring drivetrain fault diagnosis gearbox multiclass classification particle swarm optimization (PSO)
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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