Bridging AI and Maintenance: Fault Diagnosis in Industrial Air-Cooling Systems Using Deep Learning and Sensor Data

This work aims towards the automatic detection of faults in industrial air-cooling equipment used in a production line for staple fibers and ultimately provides maintenance scheduling recommendations to ensure seamless operation. In this context, various deep learning models are tested to ultimately define the most effective one for the intended scope. In the examined system, four vibration and temperature sensors are used, each positioned radially on the motor body near the rolling bearing of the motor shaft—a typical setup in many industrial environments. Thus, by collecting and using data from the latter sources, this work exhaustively investigates the feasibility of accurately diagnosing faults in staple fiber cooling fans. The dataset is acquired and constructed under real production conditions, including variations in rotational speed, motor load, and three fault priorities, depending on the model detection accuracy, product specification, and maintenance requirements. Fault identification for training purposes involves analyzing and evaluating daily maintenance logs for this equipment. Experimental evaluation on real production data demonstrated that the proposed ResNet50-1D model achieved the highest overall classification accuracy of 97.77%, while effectively resolving the persistent misclassification of the faulty impeller observed in all the other models. Complementary evaluation confirmed its robustness, cross-machine generalization, and suitability for practical deployment, while the integration of predictions with maintenance logs enables a severity-based prioritization strategy that supports actionable maintenance planning.deep learning; fault classification; industrial air-cooling; industrial automation; maintenance scheduling; vibration analysis