English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6480/11652
Visitors : 20228931      Online Users : 288
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/18371

Title: Fault Diagnosis for Internal Combustion Engines Using Intake Manifold Pressure and Artificial Neural Network
Authors: Wu, Jian-Da;Huang, Cheng-Kai;Chang, Yo-Wei;Shiao, Yao-Jung
Contributors: 車輛科技研究所
Keywords: Intake manifold pressure;Discrete wavelet transform;Artificial neural network;Fault diagnosis
Date: 2010-03
Issue Date: 2014-04-29T07:28:20Z
Publisher: Elsevier Ltd
Abstract: This paper describes an internal combustion engine fault diagnosis system using the manifold pressure of the intake system. The manifold pressure of the engine intake system always demonstrates the engine condition and affects the volumetric efficiency, fuel consumption and performance of internal combustion engines. Manifold pressure is well known to be detrimental to engine system stability and performance and it must be considered during regular maintenance. Conventional engine diagnostic technology using manifold pressure in intake system already exists through analyzing the differences between signals and depends on the experience of the technician. Obviously, the conventional detection is not a precise approach for manifold pressure detection when the engine in operation condition. In the present study, a system consisted of manifold pressure signal feature extraction using discrete wavelet transform (DWT) and fault recognition using the neural network technique is proposed. To verify the effect of the proposed system for identification, both the radial basis function network (RBFN) and generalized regression neural network (GRNN) are used and compared in this study. The experimental results indicated the proposed system using manifold pressure signal as data input is effective for engine fault diagnosis in the experimental engine platform.
Relation: Expert Systems with Applications, 37(2): 949-958
Appears in Collections:[車輛科技研究所] 期刊論文

Files in This Item:

File SizeFormat
index.html0KbHTML339View/Open


All items in NCUEIR are protected by copyright, with all rights reserved.

 


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback