Diesel Engine Fault Diagnosis Based on Rough Sets

Hong Xia Pan; Jing Yi Tian

doi:10.4028/www.scientific.net/AMR.722.276

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 722Diesel Engine Fault Diagnosis Based on Rough Sets

Diesel Engine Fault Diagnosis Based on Rough Sets

Abstract:

This paper introduces the rough set theory and ROSETTA software characteristics, gives a diesel engine fault diagnosis system based on rough set theory and the vibration signal of cylinder head. Taking a certain type large power diesel engine as an example, the first to be extracted from the cylinder head vibration signal wavelet packet de-noising and time-frequency domain analysis, constructed eigenvalue for fault diagnosis, then use ROSETTA software reduction feature attributes, finally completed fault pattern classification through the neural network. By comparing the output results of the neural network before and after processing by the ROSETTA software, show that rough set theory can optimize the feature attributes, effectively reduce the input of the neural network nodes, and improve the fault classification accuracy.

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Periodical:

Advanced Materials Research (Volume 722)

Pages:

276-281

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.722.276

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Online since:

July 2013

Authors:

Hong Xia Pan, Jing Yi Tian

Keywords:

Fault Diagnosis, Neural Network (NN), ROSETTA, Rough Set (RS), Wavelet Packets De-Noising

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