Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation

• Published in: Informatics (Basel) Vol. 12; no. 1; p. 25
• Main Authors: Srinivaas, A., Sakthivel, N. R., Nair, Binoy B.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Accuracy; Acoustics; Algorithms; Artificial neural networks; Bearings; classifiers; Combustion; Control algorithms; Data analysis; Data mining; Deep learning; Efficiency; Engine components; Engines; Fault detection; Fault diagnosis; Fault location (Engineering); Faults; feature engineering; Fourier transforms; Genetic algorithms; Internal combustion engines; Lubricants & lubrication; Machine learning; Methods; Neural networks; Pattern recognition; predictive modeling; Researchers; Sensors; Statistical analysis; supervised learning; Support vector machines; Thermography; Vibration analysis; Wavelet transforms; India
• ISSN: 2227-9709; 2227-9709
• DOI: 10.3390/informatics12010025

Fault diagnostics in internal combustion engines (ICEs) is vital for optimal operation and avoiding costly breakdowns. This paper reviews methodologies for ICE fault detection, including model-based and data-driven approaches. The former uses physical models of engine components to diagnose defects, while the latter employs statistical analysis of sensor data to identify patterns indicating faults. Various methods for ICE fault identification, such as vibration analysis, thermography, acoustic analysis, and optical approaches, are reviewed. This paper also explores the latest approaches for detecting ICE faults. It highlights the challenges in the diagnostic process and ways to enhance result accuracy and reliability. This paper concludes with a review of the progress in fault identification in ICE components and prospects, highlighted by an experimental investigation using 16 machine learning algorithms with seven feature selection techniques under three load conditions to detect faults in a four-cylinder ICE. Additionally, this study incorporates advanced deep learning techniques, including a deep neural network (DNN), a one-dimensional convolutional neural network (1D-CNN), Transformer and a hybrid Transformer and DNN model which demonstrate superior performance in fault detection compared to traditional machine learning methods.