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Abstract
The fault diagnosis on diesel engine is a difficult problem due to the complex structure of the engine and the presence of multi-excite sources. A new kind of fault diagnosis system based on Rough Set Theory and Support Vector Machine is proposed in the paper. Integrating the advantages of Rough Set Theory in effectively dealing with the uncertainty information and Support Vector Machine’s greater generalization performance. The diagnosis of a diesel demonstrated that the solution can reduce the cost and raise the efficiency of diagnosis, and verified the feasibility of engineering application.
This work was Supported by the National Natural Science foundation of China (No.10371131).
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Authors and Affiliations
College of Science, China Agricultural University, 100083, Beijing, China
Yitian Xu & Laisheng Wang
- Yitian Xu
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- Laisheng Wang
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Editors and Affiliations
School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore
Lipo Wang
Honda Research Institute Europe GmbH, Offenbach/Main, Germany
Yaochu Jin
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Xu, Y., Wang, L. (2005). Fault Diagnosis System Based on Rough Set Theory and Support Vector Machine. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_124
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