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LIU Guanjun
LIU Xinmin
QIU Jing
HU Niaoqing
College of
Mechatronics Engineering
and Automation,
National University of Defense
Technology,
Changsha 410073, Chin |
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FAULT DIAGNOSIS APPROACH BASED ON
HIDDEN MARKOV MODEL AND
SUPPORT VECTOR MACHINE*
Abstract:
Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox’s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.
Key words:
Hidden Markov model Support vector machine Fault diagnosis |