Inertial load classification of low-cost electro-mechanical systems under dataset shift with fast end of line testing. (October 2021)

• Record Type: Journal Article
• Authors: Valceschini, Nicholas; Mazzoleni, Mirko; Previdi, Fabio
• Is Part Of: Engineering applications of artificial intelligence. Volume 105(2021)
• Publication Date: 2021-10
• Subjects: Classification -- Dataset shift -- EMA -- End of line testing; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104446
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.