An Interactive and Adaptive Learning Cyber Physical Human System for Manufacturing With a Case Study in Worker Machine Interactions

Abstract

An adaptive machine learning (ML) based smart manufacturing interactive cyber physical human system (ICPHS) is conceptualized, designed, and implemented. One of its significant properties is an ML model that during deployment self-evolves with the streaming data in a self-labeling manner. This automated adaptive ML system is realized by leveraging the underlying causality during human machine interactions, and initialized by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> domain knowledge and preprocessed public datasets. The system defines a causal and temporal mapping of worker and machine states where one side can label the other automatically. A case study in machines with different automation levels is conducted in a multiuser facility by using finite state machine representations for machine energy states, worker activity states, and interaction transition functions. The fully automated adaptive ML system improves its accuracy for human machine interaction detection by up to 12.5% and shows potential to recognize more fine-grained actions.