An integrated IoT framework for intelligent vibration-based fault diagnosis of internal combustion engines

This study presents an intelligent IoT-based approach to vibration diagnostics of internal combustion engines using a wireless sensing architecture for real-time condition monitoring. The proposed system enables continuous acquisition of vibration signals with remote data transmission to a server, where signal preprocessing, Kalman filtering, root mean square (RMS) computation, frequency-domain analysis using Fast Fourier Transform, and multidimensional feature extraction are performed. Experimental validation was conducted on a laboratory test bench under healthy and artificially induced outer-ring bearing defect conditions at rotational speeds of 500, 1000, and 1500 rpm. Classical vibration analysis revealed a consistent 34–37% increase in RMS values in the presence of a defect, along with characteristic spectral components corresponding to the calculated fault frequencies. To enhance diagnostic robustness, a supervised machine learning framework was implemented using time- and frequency-domain features. Comparative evaluation demonstrated that machine learning models significantly outperform conventional RMS threshold-based diagnostics. The Random Forest classifier achieved up to 97% classification accuracy (ROC-AUC = 0.99) under random split validation and maintained 94% accuracy under cross-speed validation, confirming strong generalization capability across varying operating regimes. The results demonstrate that integrating IoT-based sensing, advanced signal processing, and intelligent classification provides a scalable and reliable solution for predictive maintenance of ICE systems in transportation, energy, and industrial applications.