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Shock and Vibration
Volume 2014, Article ID 823514, 9 pages
Research Article

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

School of Mechanical Engineering, Sharif University of Technology, Azadi Street, Tehran 145888-9694, Iran

Received 9 May 2013; Accepted 19 September 2013; Published 25 February 2014

Academic Editor: Gyuhae Park

Copyright © 2014 S. M. Jafari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 ( , 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.