The Journal of Engine Research

The Journal of Engine Research

Convolutional transformer approach for engine spark plug fault diagnosis using acoustic signal

Document Type : Original Article

Authors
1 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 Department of Agricultural Engineering, Technical and Vocational University (TVU), Tehran, Iran
Abstract
Detecting and rectifying spark plug faults are pivotal in preventing engine-related issues that can have substantial operational and financial consequences. To improve the accuracy and robustness of spark plug fault diagnosis, this research introduces a novel Convolutional Transformer approach that leverages the strengths of Convolutional Neural Networks and Transformers, which effectively capture both local and extended temporal dependencies within spark plug acoustic signals. The results of this groundbreaking approach, as presented in accompanying tables and figures, demonstrate its superior performance, achieving an impressive 97.1% accuracy in a challenging 4-class classification scenario using solely acoustic signals. This achievement signifies a significant advancement in spark plug fault detection, potentially ushering in more reliable and precise diagnostic methods, ultimately contributing to the prevention of costly engine breakdowns and the extension of engine lifespan. Deep learning techniques such as Convolutional Transformers offer a promising way to improve the reliability and performance of internal combustion engines as the automotive industry continues to evolve, highlighting the importance of this research for future automotive developments.
Keywords

[1] Javan S, Hosseini S, Alaviyoun S, Ommi F. Effect of electrode erosion on the required ignition voltage of spark plug in CNG spark ignition engine. The Journal of Engine Research, 2022;26(26):31-9.
[2] Moosavian S, Najafi G, Ghobadian B, Jafari S, Sakhaei B, Khazaee M. Fault diagnosis in engine spark plug by vibration analysis using neural network. The Journal of Engine Research, 2022;28(28):21-9. [In Persian]
[3] Azrin AA, Yusri IM, Sudhakar K, Mohd W, A Zainal, Majeed A. An overview of the spark plug engine profile in a spark ignition engine. IOP Conf Ser: Mater Sci Eng. 2021 Mar 1;1092(1):012030. doi: 10.1088/1757-899x/1092/1/012030
[4] Han J, Yamashita H, Hayashi N. Numerical study on the spark ignition characteristics of hydrogen-air mixture using detailed chemical kinetics. International Journal of Hydrogen Energy. 2011 Jul;36(15):9286–97. doi: 10.1016/j.ijhydene.2011.04.190
[5] Vong CM, Wong PK, Wong KI. Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis. Applied Soft Computing. 2014 Sep 1;22:238–48. doi: 10.1016/j.asoc.2014.05.014
[6] Shen Z, Chen X, Zhang X, He Z. A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM. Measurement. 2012 Jan;45(1):30–40. doi: 10.1016/j.measurement.2011.10.008
[7] Khazaee M, Ahmadi H, Omid M, Banakar A, Moosavian A. Feature-level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals. Insight-Non-Destructive Testing and Condition Monitoring. 2013 Jun 1;55(6):323–30. doi: 10.1784/insi.2012.55.6.323
[8] Basir O, Yuan X. Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Information Fusion. 2007 Oct;8(4):379–86. doi: 10.1016/j.inffus.2005.07.003
[9] Xiao R, Hu Q, Li J. Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine. Measurement. 2019 Nov;146:479–89. doi: 1016/j.measurement.2019.06.050
[10] Zarei J, Tajeddini MA, Karimi HR. Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics. 2014 Mar;24(2):151–7. doi: 10.1016/j.mechatronics.2014.01.003
[11] Huangfu Y, Seddik E, Habibi S, Wassyng A, Tjong J. Fault Detection and Diagnosis of Engine Spark Plugs Using Deep Learning Techniques. SAE International Journal of Engines. 2021 Nov 10;15(4):515–25. doi: 10.4271/03-15-04-0027
[12] Moosavian A, Khazaee M, Najafi G, Khazaee M, Sakhaei B, Jafari SM. Wavelet denoising using different mother wavelets for fault diagnosis of engine spark plug. Proceedings of the Institution of Mechanical Engineers Part E, Journal of Process Mechanical Engineering. 2015 Jul 21;231(3):359–70. doi: 10.1177/0954408915595952
[13] Moosavian A, Khazaee M, Najafi G, Kettner M, Mamat R. Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory. Applied Acoustics. 2015 Jun;93:120–9. doi: 10.1016/j.apacoust.2015.01.008
[14] Wu H, Triebe MJ, Sutherland JW. A transformer-based approach for novel fault detection and fault classification/diagnosis in manufacturing: A rotary system application. Journal of Manufacturing Systems. 2023 Apr;67:439–52. doi: 10.1016/j.jmsy.2023.02.018
[15] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention Is All You Need [Internet]. arXiv.org. 2017.
[16] Ahmed H, Nandi AK. Convolutional-Transformer Model with Long-Range Temporal Dependencies for Bearing Fault Diagnosis Using Vibration Signals. Machines. 2023 Jul 17;11(7):746–6. doi: 10.3390/machines11070746
[17] Mofleh A, Shmroukh A, Ghazaly N. Fault detection and classification of spark ignition engine based on acoustic signals and artificial neural network. Int J Mech Prod Eng Res Dev. 2020 Jul 30;10:5571–8.
Volume 70, Issue 4 - Serial Number 73
English Paper
Winter 2024
Pages 56-68

  • Receive Date 02 April 2024
  • Revise Date 11 May 2024
  • Accept Date 16 May 2024