Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation
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,...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/12/1/25 |
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| author | A. Srinivaas N. R. Sakthivel Binoy B. Nair |
| author_facet | A. Srinivaas N. R. Sakthivel Binoy B. Nair |
| author_sort | A. Srinivaas |
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| description | 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. |
| format | Article |
| id | doaj-art-f5a0aa8fe0614072a8f25ea4f1a35dd8 |
| institution | Kabale University |
| issn | 2227-9709 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj-art-f5a0aa8fe0614072a8f25ea4f1a35dd82025-08-20T03:43:16ZengMDPI AGInformatics2227-97092025-02-011212510.3390/informatics12010025Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental InvestigationA. Srinivaas0N. R. Sakthivel1Binoy B. Nair2Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, IndiaDepartment of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, IndiaDepartment of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, IndiaFault 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.https://www.mdpi.com/2227-9709/12/1/25internal combustion enginesmachine learningclassifiersfeature engineeringpredictive modelingsupervised learning |
| spellingShingle | A. Srinivaas N. R. Sakthivel Binoy B. Nair Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation Informatics internal combustion engines machine learning classifiers feature engineering predictive modeling supervised learning |
| title | Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation |
| title_full | Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation |
| title_fullStr | Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation |
| title_full_unstemmed | Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation |
| title_short | Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation |
| title_sort | machine learning approaches for fault detection in internal combustion engines a review and experimental investigation |
| topic | internal combustion engines machine learning classifiers feature engineering predictive modeling supervised learning |
| url | https://www.mdpi.com/2227-9709/12/1/25 |
| work_keys_str_mv | AT asrinivaas machinelearningapproachesforfaultdetectionininternalcombustionenginesareviewandexperimentalinvestigation AT nrsakthivel machinelearningapproachesforfaultdetectionininternalcombustionenginesareviewandexperimentalinvestigation AT binoybnair machinelearningapproachesforfaultdetectionininternalcombustionenginesareviewandexperimentalinvestigation |