Fault Diagnosis in Mechanical Systems Using IoT and Machine Learning

Abstract

Mechanical system failures in industrial environments can lead to costly downtime, safety hazards, and reduced productivity. Fault diagnosis is essential to ensure reliability and optimal performance. Recent advancements in IoT and machine learning have enabled real-time fault detection and predictive maintenance. This paper reviews fault diagnosis methodologies using IoT-enabled sensors, data acquisition techniques, and machine learning algorithms. Key topics include vibration and acoustic sensing, feature extraction, supervised and unsupervised learning models, deep learning approaches, and practical applications in rotating machinery, gearboxes, and pumps. Case studies demonstrate significant improvements in fault detection accuracy and reduced downtime. Challenges such as sensor integration, data quality, and cybersecurity are also discussed. The study concludes that IoT and machine learning-based fault diagnosis is critical for efficient and reliable industrial mechanical systems.

Cite as:

Dr. Kondekal Manjunatha. (2025). Fault Diagnosis in Mechanical Systems Using IoT and Machine Learning. Recent Trends in Automation and Automobile Engineering, 8(3), 49–53. 

https://doi.org/10.5281/zenodo.17650552