Abstract
PurposeThe health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.Design/methodology/approachThe proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.FindingsThe analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.Originality/valueThis study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.
Subject
Computer Science Applications,History,Education
Reference37 articles.
1. Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems;Journal of Manufacturing Systems,2017
2. A deep learning-based social distance monitoring framework for COVID-19;Sustainable Cities and Society,2021
3. Data-driven cyber-physical system framework for connected resistance spot welding weldability certification;Robotics and Computer-Integrated Manufacturing,2021
4. An application of cyber-physical system and multi-agent technology to demand-side management systems;Pattern Recognition Letters,2021
5. Bagade, P., Banerjee, A. and Gupta, S.K.S. (2017), “Validation, verification, and formal methods for cyber-physical systems”, Cyber-Physical Systems, Elsevier, pp. 175-191.