Title :
Fault diagnosis for gearbox based on genetic-SVM classifier
Author :
Xu, Yunjie ; Li, Wenbin
Author_Institution :
Eng. Coll., Beijing Forestry Univ., Beijing, China
Abstract :
Failure of gearbox is very complex, so it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on genetic-support vector machine (GSVM) approach is presented for fault diagnosis of gearbox. The performance of the GSVM system proposed in this study is evaluated by gearbox in the wood-wool working device. The test results show that this GSVM model is effective to detect failure of gearbox in the wood-wool working device.
Keywords :
failure (mechanical); fault diagnosis; gears; genetic algorithms; mechanical engineering computing; pattern classification; support vector machines; wood; woodworking machines; wool; GSVM system; failure detection; fault diagnosis; gearbox; genetic-SVM classifier; genetic-support vector machine; intelligent diagnostic method; wood-wool working device; Artificial neural networks; Biological cells; Educational institutions; Fault diagnosis; Forestry; Genetic engineering; Kernel; Risk management; Support vector machine classification; Support vector machines; fault diagnosis; genetic-support vector machine; kernel function parameter; wood-wool working device;
Conference_Titel :
Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-5585-0
Electronic_ISBN :
978-1-4244-5586-7
DOI :
10.1109/ICCAE.2010.5451933
