Back to Search Start Over

Data-driven operator functional state classification in smart manufacturing.

Data-driven operator functional state classification in smart manufacturing.

Authors :
Besharati Moghaddam, Fatemeh
Lopez, Angel J.
Van Gheluwe, Casper
De Vuyst, Stijn
Gautama, Sidharta
Source :
Applied Intelligence; Dec2023, Vol. 53 Issue 23, p29140-29152, 13p
Publication Year :
2023

Abstract

One of the main challenges in the industry is having trained and efficient operators in manufacturing lines. Smart adaptive guidance systems are developed that offer assistance to the operator during assembly. Depending on the operator's level of execution, the system should be able to serve a different guidance response. This paper investigates the assessment and classification of the operator's functional state using observed task execution times. Five different classifiers are studied for operator functional state classification on task execution time series. The experiments are based on an industry case and the ground truth is provided by an expert rule-based system. Three classification scenarios are defined that segment the problem on the level of the task, the individual, or the team. Furthermore, the investigation includes the evaluation of four distinct window-size configurations. The examination of how these scenarios and window-sizes influence the studied dataset across diverse classifiers reveals that achieving enhanced accuracy necessitates a larger input dimension. In this context, Convolutional Neural Networks predominantly exhibit superior performance compared to alternative classifiers. Careful attention needs to be paid to performance over classes and skills, but results confirm the validity of the approach for data-driven operator functional state classification. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0924669X
Volume :
53
Issue :
23
Database :
Complementary Index
Journal :
Applied Intelligence
Publication Type :
Academic Journal
Accession number :
173923725
Full Text :
https://doi.org/10.1007/s10489-023-05059-5