FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE |
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Authors: | LIU Guanjun LIU Xinmin QIU Jing HU Niaoqing |
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Affiliation: | College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073, China |
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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. |
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Keywords: | Hidden Markov model Support vector machine Fault diagnosis SUPPORT VECTOR MACHINE HIDDEN MARKOV MODEL BASED APPROACH DIAGNOSIS result methods better accuracy small training faults diagnostic used monitor diagnose features vibration gearbox experiment |
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