Abstract

The need for constantly efficient machines or systems and the increased costs associated with maintaining such efficiency has gradually changed the way in which maintenance of machines or devices is carried out. Such activities are, in fact, increasingly dependent on technologically advanced systems capable of predicting component deterioration over the course of regular use. In order to optimize maintenance interventions, therefore, the need arises to be able to predict the behavior of artifacts by monitoring the wear status of the component under operating conditions. This aspect is particularly felt in cases where the variables that determine deterioration are difficult to predict in their frequency while knowing the type of external actions that lead to deterioration. Current monitoring technologies rely primarily on the use of sensors, coupled with the relative ease of being able to manage data and share it in real time on the cloud, which is increasingly leading companies to equip their devices with sensor networks capable of recording a wide range of signals to monitor the health of the device itself. This paper reports on the preliminary development phase of a fatigue testing machine for testing instrumented seats using a Cartesian manipulator.

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