Research Status of Fault Diagnosis Based on Support Vector Machine

Abstract:

Support vector machine has good learning ability and it is good to perform the structural risk minimization principle of statistical learning theory and its application in fault diagnosis of the biggest advantages is that it is suitable for small sample decision. Its nature of learning method is under the condition of limited information to maximize the implicit knowledge of classification in data mining and it is of great practical significance for fault diagnosis. This paper analyzed and summarized the present situation of application of support vector machine in fault diagnosis and made a meaningful exploration on development direction of the future.

• Periodical: Applied Mechanics and Materials (Volumes 475-476)
• Pages: 787-791
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.475-476.787
• Online since: December 2013
• Authors: Li Mei Liu, Jian Wen Wang, Ying Guo, Hong Sheng Lin
• Keywords: Fault Diagnosis, Imbalanced Data Sets, Multiclass Classification, Support Vector Machine (SVM)

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