Fault diagnosis for large diesel fuel engine based on chaotic fractal method
Author(s) -
Jie Li,
Jian min Zhao,
Xu An Wang
Publication year - 2016
Publication title -
journal of algorithms and computational technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.234
H-Index - 13
eISSN - 1748-3026
pISSN - 1748-3018
DOI - 10.1177/1748301816640252
Subject(s) - lyapunov exponent , chaotic , diesel engine , correlation dimension , control theory (sociology) , fault (geology) , fractal , wavelet , fractal dimension , computer science , scaling , mathematics , algorithm , automotive engineering , engineering , mathematical analysis , artificial intelligence , geometry , control (management) , seismology , geology
In contrast with the large diesel engine fuel system operational condition complexity, the chaotic fractal characteristics in the combustion process of the diesel engine fuel system are systematically analyzed. Then, based on chaotic fractal method, an effective fault diagnosis method is further proposed. And under the two kinds of typical failure modes, the two important characteristic parameters variation trend for chaotic system, namely, correlation dimensions and the calculation methods of the maximum Lyapunov exponent are discussed. On the process of chaotic parameters calculation the wavelet noise reduction method is used to handle the original signal to conquer the linear scaling region properly. The results show that the maximum Lyapunov exponent method is a more effective method on the diesel engine fuel system fault diagnosis than the correlation dimension method, which can be further used in health condition assessment for this type fuel engine.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom