Sensor fusion for more accurate features in condition monitoring of mechatronic systems

The paper presents and analyzes critically a theoretical and experimental methodology, able to define and characterize features for condition monitoring of mechatronic systems. A kinematic and dynamic model of the whole mechatronic system is realized, allowing us to identify the phases of the operating cycle, which are the most meaningful from the condition monitoring point of view. Furthermore, it allows us to evaluate a set of quantities of interest throughout the whole chain, when both the measured quantity and the monitoring point are changed along the kinematic chain, from the motor to the end effector. The analysis is carried on in order to evaluate the most reliable configuration as for measuring point and quantity for condition monitoring. Selectivity and repeatability of features are evaluated experimentally, when operating conditions of the systems are changed, in order to realize the best features database for a possible processing by Artificial Neural Network.