Title :
Mechanical fault diagnose of diesel engine based on bispectrum and Support Vector Machines
Author :
Xiao, Yunkui ; Mei, Jianmin ; Zeng, Ruili ; Zhao, Huimin ; Tang, Li ; Huang, Huafei
Author_Institution :
Dept. of Automotive Eng., Acad. of Mil. Transp., Tianjin, China
Abstract :
The vibrant signal of diesel engine is analyzed by the method of bispectrum, and bispectral feature planes are searched along diagonal and parallel lines of diagonal at certain step in the bispectral modulus field, and the mean breadth value in bispectral feature planes are calculated as signal feature which is capable of describing the fault. The support vector machines is used to diagnose the fault successfully by importing signal features as training samples. The experiment results show that, the noise in the vibrant signal of diesel engine can be eliminated by bispectrum and the signal feature can be extracted effectively; The signal features are perfectly described by feature planes and exist not only on diagonal and but also in the field besides the diagonal; support vector machines can study effectively and diagnose successfully with limited fault samples.
Keywords :
diesel engines; fault diagnosis; mechanical engineering computing; support vector machines; bispectral modulus field; bispectrum method; diesel engine; mechanical fault diagnose; signal feature extraction; support vector machines; Automotive engineering; Diesel engines; Fault diagnosis; Feature extraction; Machinery; Pistons; Signal analysis; Statistical learning; Support vector machines; Transportation; Bispectrum; Diagnose; Diesel Engine; Support Vector Machines;
Conference_Titel :
Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-4519-6
Electronic_ISBN :
978-1-4244-4520-2
DOI :
10.1109/ICCSIT.2009.5234916