The Application of Binary Tree-Based Fuzzy SVM Multi-Classification Algorithm to Fault Diagnosis on Modern Marine Main Engine Cooling System
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.
binary tree FSVM the cooling water system of ships fault diagnosis
Yulong Zhan HuiqingYang Qinming Tan Yao Yu
Department of Marine Engineering Shanghai Maritime University Shanghai, China Shanghai Research and Development Center Lubricating Oil Group Co Sinopec Shanghai,China Department of Marine Engineering Shanghai Maritime University Shanghai,China
国际会议
北京
英文
54-57
2010-09-18（万方平台首次上网日期，不代表论文的发表时间）