Journal of Theoretical and Applied Vibration and Acoustics

Journal of Theoretical and Applied Vibration and Acoustics

Detection of malfunction in ignition system for an internal combustion engine via artificial intelligence model

Document Type : H. Ahmadian Prize

Authors
Mohamad Gohari 1
Abbas Pak 2
Masoud Kazemi 3
1 Assistant Professor, School of Mechanical Engineering, Arak University of Technology, Arak, IRAN
2 Assistant Professor, Department of Engineering, Bu-Ali Sina University, Hamedan, IRAN
3 M.Sc. Student, School of Mechanical Engineering, Arak University of Technology, Arak, IRAN
10.22064/tava.2024.555697.1206
Abstract
Engine failure is a significant issue for drivers, often requiring substantial experience to identify and troubleshoot effectively. Repairing the engine based on probable causes and uncertainties can be time-consuming and costly. Recently, AI models, particularly those based on Artificial Neural Networks (ANN), have been developed and gained popularity in fault diagnosis. This paper considers two common faults in internal combustion engines - cylinder misfire and complete cylinder failure - caused by ignition system issues. An Artificial Neural Network fed by Statistical features (SANN) is employed to distinguish these two faults. The SANN was trained on statistical features derived from vibration signals and achieved an accuracy of over 90%. Thus, SANN can classify the fault generated by the ignition system. This model was further validated using a different engine as a second case, demonstrating its ability to predict fault types with acceptable accuracy. In fact, the SANN could find a malfunction of the engine mounted on a car perfectly. This capability enables operators to accurately identify the type of fault, allowing for more precise and efficient repairs. Therefore, the proposed method is well-suited for troubleshooting ignition system malfunctions and diagnosing related issues via a reliable fault detection model

Highlights

  • Malfunctions of engine ignition system are detected by artificial neural network.
  • ANN algorithms are fed by statistical features derived from vibration signal.
  • Statistical features derived from vibration signal are used for training.
  • ANN algorithm presents 90% average accuracy for fault detection.
  • ANN algorithm is developed by future improvements.

Keywords
Malfunction of Ignition System
Artificial Intelligent Model
Statistical features
Internal Combustion Engine
Subjects
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Journal of Theoretical and Applied Vibration and Acoustics
Volume 8, Issue 2
July 2022
Pages 1-20