Predictive Maintenance based on Vibrational Analysis and Machine Learning in RDM Group

The advent of Industry 4.0 has significantly transformed the manufacturing industry landscape with the introduction of interconnected systems, advanced automation and real-time data acquisition. In this context, Predictive Maintenance (PdM) has gained increasing relevance as a key component to ensure business continuity and competitiveness of companies. Maintenance is now at the heart of business strategy. An unexpected failure in an industrial environment results into significant losses for companies as it affects production. In fact, an unexpected failure leads to the sudden interruption of production and can therefore have a negative impact on the product supply to customers, customer satisfaction and the company's reputation. In addition, a failure leads to significant damage to the machine or plant, increasing maintenance costs and can seriously compromise the safety of operators. Predictive Maintenance aims to prevent these failures by anticipating them. The benefits of adopting a PdM approach are numerous and include reduced machine downtime, significant savings in maintenance costs, improved safety in the working environment, improved reliability and production quality, and more precise planning of maintenance activities. One of the main challenges of PdM is to design and develop an embedded intelligent system to monitor and predict the health status of the machine. This project aims to examine the implementation of a predictive maintenance model based on the concrete application of vibration analysis and Machine Learning techniques, through a case study conducted at the RDM Group company. Intelligent predictive monitoring and smart decision-making processes have become a crucial requirement for the company to safeguard industrial assets from damages that would compromise the achievement of business objectives and that would result in a loss of competitiveness. The main goal of this research is to address the challenges inherent in maintenance in an environment where early recognition of potential failures is critical. Vibrational analysis represents a powerful and effective diagnostic methodology for monitoring the condition of industrial machines, enabling the early identification of anomalies and signals of imminent faults. The combined use of Machine Learning algorithms offers the opportunity to obtain accurate predictions and become an important and supportive tool for decision-making processes. Through a detailed analysis of machine vibrations and the use of Machine Learning algorithms, this research intends to develop a fault recognition model capable of anticipating imminent faults and optimizing maintenance. The case study within the RDM Group provides a concrete and illustrative overview of the challenges and opportunities arising from the implementation of a Predictive Maintenance strategy in the context of Industry 4.0.