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.
Creative Commons License
Journal of Vibroengineering
Table of Contents Full-text Download PDF Acknowledgements References
Cite this article
Views 219
Reads 115
Downloads 1392

Our Conferences

62nd Conference in Almaty, Kazakhstan
February 10-11, 2023 in Almaty, Kazakhstan
63rd Conference in Shanghai, China
May 18, 2023 in Shanghai, China
64th Conference in Istanbul, Turkey
September 29, 2023 in Istanbul, Turkey
65th Conference in Resita, Romania
October 20-21, 2023 in Resita, Romania
www.jveconferences.com
Abstract.

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.

Acknowledgements

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.

References

  1. Rafiee J., Arvani F., Harifi A., Sadeghi M. H. Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical Systems and Signal Processing, Vol. 21, 2007, p. 1746-1754. [Search CrossRef]
  2. Weidong Li, Robert M. Parkin, Joanne Coy, Fengshou Gu Acoustic based condition monitoring of a diesel engine using self-organizing map networks. Applied Acoustics, Vol. 63, 2001, p. 699-711. [Search CrossRef]
  3. Geng Z., Chen J. Investigation into piston-slap-induced vibration for engine condition simulation and monitoring. Journal of Sound and Vibration, Vol. 282, 2004, p. 735-751. [Search CrossRef]
  4. Shirazi F. A., Mahjoob M. J. Application of discrete wavelet transform (DWT) in combustion failure detection of IC engines. Proceedings of the 5th International Symposium on image and Signal Processing and Analysis, 2007. [Search CrossRef]
  5. Bao-jia Chen, Li Li, Xin-ze Zhao Fault diagnosis method integrated on scale-wavelet power spectrum, rough set and neural network. Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2007. [Search CrossRef]
  6. Junxing Hou, Xinqi Qiao, Zhen Wang, Wei Liu, Zhen Huang Characterization of knocking combustion in HCCI DME engine using wavelet packet transform. Applied Energy, Vol. 87, 2009, p. 1239-1246. [Search CrossRef]
  7. Jian-Da Wua, Cheng-Kai Huang, Yo-Wei Chang, Yao-Jung Shiao Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network. Expert Systems with Applications, Vol. 37, 2009, p. 949-958. [Search CrossRef]
  8. Yujun Li, Peter W.Tse, Xin Yang, Jianguo Yang EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine. Mechanical Systems and Signal Processing, Vol. 24, 2009, p. 193-210. [Search CrossRef]
  9. Meng-Hui Wang, Kuei-Hsiang Chao, Wen-Tsai Sung, Guan-Jie Huang Using ENN-1 for fault recognition of automotive engine. Expert Systems with Applications, Vol. 37, 2010, p. 2943-2947. [Search CrossRef]
  10. Li-Fang Kong, Rong-Ling Shi, Zhang Tian, Wei Hao The vibration parameter fault diagnosis cloud model for automobile engine based on ANFIS. International Conference on Computational Intelligence and Software Engineering, Wuhan, 2010. [Search CrossRef]
  11. Vong C. M., Wong P. K. Engine ignition signal diagnosis with wavelet packet transform and multi-class least squares support vector machines. Expert Systems with Applications, Vol. 38, 2011, p. 8563-8570. [Search CrossRef]
  12. Jian-Da Wu, Cheng-Kai Huang An engine fault diagnosis system using intake manifold pressure signal and Wigner-Ville distribution technique. Expert Systems with Applications, Vol. 38, 2011, p. 536-544. [Search CrossRef]
  13. Brian D. Ripley Pattern Recognition and Neural Networks. Cambridge University Press, 2008. [Search CrossRef]
JVE Journals is rebranding to Extrica

Inspired by innovations from the previous century and the rapid growth during the last years, we are improving for excellence in your publishing experience

Read to know more
JVE Journals
Extrica