Support vector machine of the coal mine machinery equipment fault diagnosis

Abstract

Support vector machine is a machine learning algorithm developed by Vapnik from the statistical learning theory for data classification via study from a small sample of fault data. For fault data it can isolate the fault categories accurately even though only has the small sample of data. In the present work, support vector machine's classification mechanism and its application in mechanical fault diagnosis are introduced. Therefore, give an instance the support vector machine makes fault classification for the coal mine scraper conveyor's faults. Last but not the least, put forward some of the shortcomings of the support vector machine and look forward to the direction of development of the support vector machine fault diagnosis in the future.