NDLI: Application for diesel engine in fault diagnose based on fuzzy neural network and information fusion

Content Provider	IEEE Xplore Digital Library
Author	Liang Guihang Wang Qiang Wang Jian Song Jingui
Copyright Year	2011
Description	Author affiliation: Shanghai General Motors, GM for General Motor, Yantai, China (Wang Jian) \|\| School of traffic, ludong university, Yantai, China (Liang Guihang; Wang Qiang; Song Jingui)
Abstract	According to a variety of diesel engine malfunctions, a method for fault diagnoses of diesel engine based on neural net work and information fusion is put forward. The model has the characteristic of fast inference speed applying fuzzy membership functions to depict the fault extent. The model of fault diagnoses is set up by using the state parameter of diesel engine as learning samples. The data from diesel engine state is identified are sample. It is verified the validity of the model of fuzzy neural network after experiments. The results show that it has a great improvement in convenient operation and facilitates to use. This method for diagnosis faults of diesel engine has more accurately. It can improve the veracity for diagnose the fault. It can also develop the optimal control of diesel engine.
Starting Page	102
Ending Page	105
File Size	738111
Page Count	4
File Format	PDF
ISBN	9781612844855
e-ISBN	9781612844862
DOI	10.1109/ICCSN.2011.6014398
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers fuzzy neural network fault diagnosis information fusion diesel engine Engines
Content Type	Text
Resource Type	Article

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