Toward a Human-Cyber-Physical System for Real-Time Anomaly Detection

Abstract

In recent years, researchers and practitioners have focused on Industry 4.0, emphasizing the role of cyber-physical systems (CPSs) in manufacturing. However, the operationalization of Industry 4.0 has presented many implementation challenges caused by the inability of available technologies to meet industry needs effectively. Furthermore, Industry 4.0 has been criticized for the absence of focus on the human component in CPSs impacting the concept of sustainability in the long run. Responding to this critique and building on the foundation of the Industry 5.0 concept, this article proposes a holistic methodology empowered by human expert knowledge for human-cyber-physical system (HCPS) implementation. The proposed novel HCPS methodology represents a more sustainable solution for companies that consists of five phases to promote the integration of human expert knowledge and cyber and physical parts empowered by big data analytics for real-time anomaly detection. Specifically, real-time anomaly detection is enabled by industrial edge computing for big data optimization, data processing, and the industrial Internet of Things (IIoTs) real-time product quality control. Finally, we implement the developed HCPS solution in a case study from the process industry, where automated system decision-making is achieved. The results obtained indicate that an HCPS, as a strategy for companies, must augment human capabilities and require human involvement in final decision-making, foster meaningful human impact, and create new employment opportunities.