Title :
Research on Fault Diagnosis of Marine Diesel Engine Based on Grey Relational Analysis and Kernel Fuzzy C-means Clustering
Author :
Xiuyan Peng ; Yanyou Chai ; Liufeng Xu ; Xinjiang Man
Author_Institution :
Coll. of Autom. Harbin Eng., Univ. Harbin, Harbin, China
Abstract :
According to the problem of small samples and nonlinear feature in fault diagnosis of marine diesel engine, comprehensively using the methods of grey relational analysis and kernel fuzzy c-means clustering, a method solving fault diagnosis of marine diesel engine is proposed. Firstly, kernel fuzzy c-means clustering was made on historical fault dataset. Secondly, the preliminary fault diagnosis was made on testing samples by using grey relational analysis and kernel fuzzy cmeans clustering separately. Finally, the final fault diagnosis results were got by the linear weighting matrix of fuzzy membership matrix and grey relational matrix. The fault diagnosis results of MAN B&W 10L90MC marine diesel engine show that this method can improve the accuracy of marine diesel engine.
Keywords :
diesel engines; fault diagnosis; grey systems; marine systems; matrix algebra; pattern clustering; fault diagnosis; fuzzy membership matrix; grey relational analysis; historical fault dataset; kernel fuzzy c-means clustering; linear weighting matrix; marine diesel engine; nonlinear feature; Accuracy; Diesel engines; Educational institutions; Fault diagnosis; Fuels; Kernel; Testing; fault diagnosis; grey relational analysis; kernel fuzzy c-means clustering; marine diesel engine;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2012 Fifth International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4673-0470-2
DOI :
10.1109/ICICTA.2012.78