Inertial load classification of low-cost electro-mechanical systems under dataset shift with fast end of line testing

This paper presents a rationale for designing a machine learning algorithm under dataset shift. In particular, we focus on the classification of the inertial load of low-cost Electro-Mechanical Actuators (EMAs) into several weight categories. In these low-cost settings, due to uncertainties in the manufacturing process, raw materials and usage, even if the EMA part number is the same, its serial numbers (i.e. items or exemplars) may show different physical behaviors. Thus, a learning model trained on data from a set of items can perform poorly when applied to other ones. The proposed solution comprises tailored normalization and cross validation procedures for training the classifier, along with suitable End Of Line (EOL) experiments for the characterization of a new produced EMA item. The approach is experimentally validated on the classification of the mass of sliding gates, using only measurements available on the gate EMA.