CLASSIFICATION OF GEAR FAULTS USING HIGHER-ORDER STATISTICS AND SUPPORT VECTOR MACHINES

• 作 者: Lai Wuxing Zhang Guicai Shi Ti
• 机 构: School of Mechanical Science and Engineering
• 出 处: 机械工程学报
• 发表日期: 2004年期
• 关键词: Support vector machine Gear Fault diagnosis Cumulant Feature extraction
• 基金项目: 国家\"攀登\"计划;国家重点基础研究发展计划(973计划)

Gears alternately mesh and detach in driving process, and then working conditions of gears are alternately changing, so they are easy to be spalled and worn. But because of the effect of additive gaussian measurement noises, the signal-to-noises ratio is low; their fault features are difficult to extract. This study aims to propose an approach of gear faults classification,using the cumulants and support vector machines. The cumulants can eliminate the additive gaussian noises, boost the signal-to-noises ratio. Generalisation of support vector machines as classifier, which is employed structural risk minimisation principle, is superior to that of conventional neural networks, which is employed traditional empirical risk minimisation principle. Support vector machines as the classifier, and the third and fourth order cumulants as input, gears faults are successfully recognized. The experimental results show that the method of fault classification combining cumulants with support vector machines is very effective.