Advanced Diagnostic Techniques for Fault Detection and Maintenance in Diesel Generators: Integration of AI and Multimodal Sensing

Diesel generators remain indispensable across numerous industrial applications due to their robustness and reliability. However, their susceptibility to faults presents significant maintenance challenges, often leading to unexpected downtime and operational inefficiencies. This review provides a comprehensive analysis of advanced fault diagnosis methods for diesel generators by integrating multimodal sensing technologies, such as vibration, sound, temperature, and exhaust gas monitoring, with artificial intelligence (AI). The paper systematically categorizes common faults, diagnostic techniques, and the role of various AI models, including machine learning and deep learning approaches, in enhancing detection accuracy and enabling predictive maintenance. Comparative evaluations are presented through quantitative tables and visual figures that benchmark AI model performance based on accuracy and error metrics. Furthermore, the review addresses current limitations in data integration, sensor reliability, and standardization, proposing practical solutions and future research directions. By aligning diagnostic models with real‐world constraints and leveraging hybrid AI structures, this work highlights the potential of intelligent systems to transform maintenance strategies in diesel‐powered infrastructures.