FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

• 作 者: LIU Guanjun LIU Xinmin QIU Jing HU Niaoqing
• 机 构: College of Mechatronics Engineering and Automation,National University of Defense Technology,Changsh
• 出 处: 机械工程学报（英文版）
• 发表日期: 2007年5期
• 关键词: Hidden Markov model Support vector machine Fault diagnosis

Aiming at solving the problems of machine-leing 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.