Exploring the Accuracy of Machine Learning and Deep Learning in Engine Knock Detection

Usman Hameed, Dr. Sohail Masood, Fawad Nasim and Dr. Arfan Jaffar
Additional contact information
Usman Hameed: Department of Computer Science, Superior University, Lahore, 54000, Pakistan
Dr. Sohail Masood: Professor, Department of Computer Science, Superior University, Lahore, 54000, Pakistan
Fawad Nasim: Department of Computer Science, Superior University, Lahore, 54000, Pakistan
Dr. Arfan Jaffar: Department of Computer Science, Superior University, Lahore, 54000, Pakistan

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 3, 203-210

Abstract: This study explores the use of machine learning for real-time detection of engine knocking, aiming to enhance early vehicle fault recognition. We extracted frequency modulation amplitude demodulation (FMAD) features from engine sound data and evaluated various machine-learning algorithms using MATLAB. The coarse decision tree algorithm emerged as the most effective, achieving a classification accuracy of 66.01%. Subsequently, by using deep learning models, we significantly improved the accuracy: a convolutional neural network (CNN) achieved 45.16%. accuracy, a deep learning recurrent neural network (RNN) model in LSTM achieved 90% accuracy, and further refinements pushed the accuracy to 93.55%. Additionally, we introduced a knock index to quantify noise levels during each engine cycle. This index, calculated from the integral of the absolute value of the first derivative of a band-pass-filtered vibration signal, provides a visual representation of knock strength. This approach shows promise for early detection of engine knocking, although further refinement of feature extraction methods and algorithm optimization is necessary for practical application. The study highlights the potential of integrating machine learning into real-time vehicle fault detection systems to improve their reliability and effectiveness.

Keywords: Machine Learning; Engine knocking; FMAD; Deep learning. (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://bbejournal.com/BBE/article/view/940/1067 (application/pdf)
https://bbejournal.com/BBE/article/view/940 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:3:p:203-210

DOI: 10.61506/01.00478

Access Statistics for this article

Bulletin of Business and Economics (BBE) is currently edited by Dr. Muhammad Irfan Chani

More articles in Bulletin of Business and Economics (BBE) from Research Foundation for Humanity (RFH) Contact information at EDIRC.
Bibliographic data for series maintained by Dr. Muhammad Irfan Chani ().