Combined fault detection and classification of internal combustion engine using neural network

Mehrdad Nouri Khajavi1 , Sayyad Nasiri2 , Abolqasem Eslami3

1Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of Iran

2Sharif University of Technology, Tehran, Islamic Republic of Iran

3Islamic Azad University of Dehdasht, Dehdasht, Islamic Republic of Iran

1Corresponding author

Journal of Vibroengineering, Vol. 16, Issue 8, 2014, p. 3912-3921.
Received 14 May 2014; received in revised form 14 July 2014; accepted 22 August 2014; published 30 December 2014

Copyright © 2014 JVE International Ltd. 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.
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Different faults in internal combustion engines leads to excessive fuel consumption, pollution, acoustic emission and wear of engine components. Detection of fault is also difficult for maintenance technicians due to broad range of faults and combination of the faults. In this research the faults due to malfunction of manifold absolute pressure, knock sensor and misfire are detected and classified by analyzing vibration signals. The vibration signals acquired from engine block were preprocessed by wavelet analysis, and signal energy is considered as a distinguishing property to classify these faults by a Multi-Layer Perceptron Neural Network (MLPNN). The designed MLPNN can classify these faults with almost 100 % efficiency.

Keywords: fault diagnosis, internal combustion engine, neural networks, energy, wavelet transform.


Authors wish to express their gratitude to the personnel of R&D department of MEGA MOTOR Company for their cooperation. Especially Dr. Azadi the head of R&D department and engineers Mr. Tagharrobi, Mr. Hajari, Mr. Omidi and Mr. Mohammadi in the R&D laboratory.


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