- Thursday, Oct 23rd, 2025
| TITLE | Intelligent Fault Diagnosis in Petrol and Diesel Engines using AI-Based Predictive Maintenance Systems: A Comprehensive Review |
|---|---|
| ABSTRACT | The increasing complexity of modern internal combustion engines and the critical need for reliable operation have driven significant advances in intelligent fault diagnosis systems. This comprehensive review examines the state-of-the-art applications of artificial intelligence (AI) and machine learning (ML) techniques for fault detection and predictive maintenance in petrol and diesel engines. We analyze recent developments in convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures for engine diagnostics, covering performance metrics ranging from 92% to >99% accuracy across various fault detection tasks. The review synthesizes findings from 160+ recent publications, identifying key sensor technologies including vibration, acoustic, thermal, and electrical measurements, along with emerging multimodal fusion approaches. We examine practical implementations across automotive, marine, and stationary power applications, highlighting the superior performance of AI-based methods over traditional rule-based diagnostics. Current research gaps include standardized benchmarking datasets, real-time edge deployment challenges, and explainability requirements for safety-critical applications. This review provides researchers and practitioners with a comprehensive understanding of current capabilities and future directions in AI-driven engine fault diagnosis. |
| AUTHOR | Sathyamurthy E, Sabarinath S, Sagar D Department of MCA, CMR Institute of Technology, Bengaluru, India |
| PUBLICATION DATE | 2025-09-09 |
| VOLUME | 12 |
| DOI | DOI:10.15680/IJARETY.2025.1204082 |
| 82_Intelligent Fault Diagnosis in Petrol and Diesel Engines using AI-Based Predictive Maintenance Systems A Comprehensive Review.pdf | |
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