Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network

Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network

This paper presents the potential of acoustic emission (AE) technique to detect valve damage in internal combustion engines. The cylinder head of a spark-ignited engine was used as the experimental setup. The effect of three types of valve damage (clearance, semicrack, and notch) on valve leakage wa...

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Main Authors: S. M. Jafari, H. Mehdigholi, M. Behzad
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2014/823514
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spelling doaj-797fb38120374d6d9c6da24afab761ef2020-11-24T21:13:25ZengHindawi LimitedShock and Vibration1070-96221875-92032014-01-01201410.1155/2014/823514823514Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural NetworkS. M. Jafari0H. Mehdigholi1M. Behzad2School of Mechanical Engineering, Sharif University of Technology, Azadi Street, Tehran 145888-9694, IranSchool of Mechanical Engineering, Sharif University of Technology, Azadi Street, Tehran 145888-9694, IranSchool of Mechanical Engineering, Sharif University of Technology, Azadi Street, Tehran 145888-9694, IranThis paper presents the potential of acoustic emission (AE) technique to detect valve damage in internal combustion engines. The cylinder head of a spark-ignited engine was used as the experimental setup. The effect of three types of valve damage (clearance, semicrack, and notch) on valve leakage was investigated. The experimental results showed that AE is an effective method to detect damage and the type of damage in valves in both of the time and frequency domains. An artificial neural network was trained based on time domain analysis using AE parametric features (AErms, count, absolute AE energy, maximum signal amplitude, and average signal level). The network consisted of five, six, and five nodes in the input, hidden, and output layers, respectively. The results of the trained system showed that the AE technique could be used to identify the type of damage and its location.http://dx.doi.org/10.1155/2014/823514
collection DOAJ
language English
format Article
sources DOAJ
author S. M. Jafari
H. Mehdigholi
M. Behzad
spellingShingle S. M. Jafari
H. Mehdigholi
M. Behzad
Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network
Shock and Vibration
author_facet S. M. Jafari
H. Mehdigholi
M. Behzad
author_sort S. M. Jafari
title Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network
title_short Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network
title_full Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network
title_fullStr Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network
title_full_unstemmed Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network
title_sort valve fault diagnosis in internal combustion engines using acoustic emission and artificial neural network
publisher Hindawi Limited
series Shock and Vibration
issn 1070-9622
1875-9203
publishDate 2014-01-01
description This paper presents the potential of acoustic emission (AE) technique to detect valve damage in internal combustion engines. The cylinder head of a spark-ignited engine was used as the experimental setup. The effect of three types of valve damage (clearance, semicrack, and notch) on valve leakage was investigated. The experimental results showed that AE is an effective method to detect damage and the type of damage in valves in both of the time and frequency domains. An artificial neural network was trained based on time domain analysis using AE parametric features (AErms, count, absolute AE energy, maximum signal amplitude, and average signal level). The network consisted of five, six, and five nodes in the input, hidden, and output layers, respectively. The results of the trained system showed that the AE technique could be used to identify the type of damage and its location.
url http://dx.doi.org/10.1155/2014/823514
work_keys_str_mv AT smjafari valvefaultdiagnosisininternalcombustionenginesusingacousticemissionandartificialneuralnetwork
AT hmehdigholi valvefaultdiagnosisininternalcombustionenginesusingacousticemissionandartificialneuralnetwork
AT mbehzad valvefaultdiagnosisininternalcombustionenginesusingacousticemissionandartificialneuralnetwork
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