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     Research Journal of Applied Sciences, Engineering and Technology


Simulating the Diesel Engine Vibration with Fuzzy Neural Network

1Sina Abroumand Azar and 2Arash Hosseinian Ahangarnejad
1Department of Mechanical Engineering, Islamic Azad University, Tabriz, Iran
2Department of Mechanical Engineering, Politecnico di Milano, Italy
Research Journal of Applied Sciences, Engineering and Technology  2014  9:1045-1051
http://dx.doi.org/10.19026/rjaset.8.1068  |  © The Author(s) 2014
Received: April 22, 2013  |  Accepted: May 18, 2013  |  Published: September 05, 2014

Abstract

This study is conducted in order to evaluate the models of artificial intelligence in predicting the level of diesel vibrations. In this study, the Artificial Neural Network (ANN) and the Adaptive Neuro Fuzzy Inference System (ANFIS) are used in order to simulate the vibration of the whole diesel engine. Vibration in the gasoline or diesel engines has been investigated according to numerous aspects so far. Noise and vibration, which occurs in the engine due to the combustion process, can make direct effects on the users. This is particularly true in the engines with large compression ratios and engines in which the combustion pressure increases rapidly. Results indicate that the vibration of Diesel engines can be predicted with reasonable accuracy by applying the smart models. The results of predicting the Artificial Neural Network are partially better than the Adaptive neuro fuzzy inference system.

Keywords:

Diesel engine , fuzzy neural network , simulation , vibration,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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