Abstract
Support Vector Machine (SVM) is widely applied to fault diagnosis of machines. However, this classification method has some weaknesses. For example, it can not separate fuzzy information, is particularly sensitive to the interference and the isolated points of the training sample, and has great demand for memory in calculation. In view of the problems mentioned above, a binary tree-based fuzzy SVM multi-classification algorithm (BTFSVM) has been put forward. This paper focuses on the study of the application of the intelligent theory BTFSVM to fault diagnosis on modern main engine cooling water system of ships. Simulation experiments show that the algorithm has strong anti-interference ability and good classification effects. Consideration can be made that it can be further applicable to the diagnosis on other mechanical faults of ships.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jiaqiang, E.: The Diagnosis and Applications of Intelligent Fault. Hunan University Press (2006)
Lin, C.-F., Wang, S.-D.: Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks 13(2), 464–471 (2002)
Chang, C., Fee, Y.-N.: Study on Multi-Classification Algorithm Based on Fuzzy Support Vector Machine. Computer Application 28(7) (2008)
Deng, N., Tian, Y.: New Method of Data Mining. Science Press, Beijing (2004)
Zhang, G., Zhang, J.: Fault Diagnosis of the Turbine Speed Control System of Multi-Level Binary Tree Classifier Based on Fuzzy Support Vector Machine. Chinese Journal of Electrical Engineering 25, 420–470 (2005)
Zhu, Z., Zhang, B.: The Application of Fuzzy Support Vector Machine to Fault Diagnosis of the Marine Diesel Engine. China Shipbuilding 47(3) (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhan, Y., Yang, H., Tan, Q., Yu, Y. (2012). The Application of Binary Tree-Based Fuzzy SVM Multi-classification Algorithm to Fault Diagnosis on Modern Marine Main Engine Cooling System. In: Wu, Y. (eds) Advanced Technology in Teaching - Proceedings of the 2009 3rd International Conference on Teaching and Computational Science (WTCS 2009). Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11276-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-11276-8_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11275-1
Online ISBN: 978-3-642-11276-8
eBook Packages: EngineeringEngineering (R0)