An approach for fault detection based on multibody simulations and feature selection algorithm

In the context of Industry 4.0, Predictive Maintenance becomes one of the main challenges for manufacturers. The monitoring of machinery health status results in huge cost savings. For this reason, the industry of automated machinery is moving in this direction, by acquiring large volumes of data, even if without full awareness of which physical variables are important to predict the status of a machine nor, consequently, which sensors to use and where to place them. This paper presents a general approach for the selection of sensors arrangement for the development of a condition monitoring system. The algorithm is based on multibody simulation tool and gives guidelines about the physical quantities to monitor and the parameters to extract. A machine learning model is then trained to demonstrate the ability of the obtained setup in identifying possible faults. The main benefit of this work regards the generality of the approach: it can be applied to different application cases (not only automated machineries), with the only constraint of developing a validated multibody model of the system.