Risk assessment model based on centrifugal governors and artificial neural networks

In today’s industry, old machines, that were not manufactured according to Industry 4.0 standards, may not be equipped with sophisticated sensors for monitoring critical values and ensuring the machine's proper health and operation. As a result, third-party sensors, such as thermometers and vibration sensors, are often integrated into these machines. Unfortunately, despite being able to obtain effective measurements, such sensors lack relativization of these values to the contextual values of each machine. This paper proposes a risk assessment model that digitally mimics a real-life centrifugal governor's operation. The system combines machine learning and data analysis and uses a context-aware algorithm that can work with single or multiple sensors to output aggregated information on a machine’s health.