Pattern recognition of diesel engine by using kernel principle component analysis and support vector machine

Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis

Volumn 29, Issue 1, 2009, Pages 42-45

  Li, Hongkun ^a   Ma, Xiaojiang ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Diesel engine; Kernel principle component analysis; Pattern recognition; Support vector machine

Indexed keywords

COMBUSTION SYSTEMS; ENGINE VIBRATIONS; FEATURE VECTORS; KERNEL PRINCIPLE COMPONENT ANALYSIS; OPERATING CONDITIONS; PARAMETER VECTORS; REDUNDANCY INFORMATIONS; SUPPORT VECTOR MACHINE;

DIESEL ENGINES; ENGINES; FEATURE EXTRACTION; IMAGE RETRIEVAL; SUPPORT VECTOR MACHINES; VECTORS;

PATTERN RECOGNITION SYSTEMS;

EID: 64249140488     PISSN: 10046801     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (4)

1. Vapnik V N. The nature of statistical learning theory[M]. Beijing: Tsinghua University Press, 2000.
2. Huang N E, Shen Z, Long S R. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences, 1998(454): 903-995.
3. Li Hongkun, Zhou Peilin, Ma Xiaojiang. Marine diesel engine fault diagnosis by using improved hilbert spectrum[J]. Journal of Ship Research, 2006(4): 378-387.