Exploiting a Machine Learning model to support the Predictive Maintenance of an engine equipped with a rotating shaft

One of the most promising applications of Industry 4.0 enabling technologies concerns the creation of systems capable of providing condition and predictive maintenance services. This thesis work deals with the introduction of the objectives of these services, their difficulties and known problems, and the solutions offered by the literature. It also describes the design and implementation of a system capable of detecting vibrations on a rotating shaft of an electric motor. A solution based on a Data-driven approach, using an accelerometer and combining machine learning models to determine the operating status of the machine and report any anomaly. Particular attention is paid to the preprocessing of data to limit the calculation costs and increase the speed of execution while maintaining high reliability.