Feasibility Study for the Development of a Diagnostic and Prognostic System on a High-Speed Rotating Cutter

The integration of artificial intelligence and advanced machine learning techniques has radically changed the safety and reliability of industrial systems. This innovative paradigm led to the widespread adoption of condition-based maintenance strategies, with vibration monitoring emerging as a milestone technique. This study, conducted in collaboration with Tecnau SRL, investigates the feasibility of implementing a diagnostic and prognostic system for their “Revolution 50” series apparatus. This work explores machinery behaviour through endurance tests to lay the foundation for the future development of anomaly detection and machinery health classification. Experimental tests facilitate continuous monitoring under various operating conditions to potentially conceive real-time industrial diagnostic systems. Endurance tests reveal promising results, showing the potential for accurate recognition of the machine state of health. Multi-scale signal analysis highlights the significance and the detection of transient and steady-state phases, improving the effectiveness of potential real-time monitoring strategies. Future research directions include further industrial development of real-time monitoring systems, optimization of classification models, and exploration of cost-effective sensor selection and acquisition systems.