The current introduction of Industry 4.0 is very challenging for industrial companies. On the one hand, there is an urge to implement concepts such as digital worker assistance systems or cyber-physical production systems, but besides theoretical work, there is very little research that shows examples of its practical implementation. Furthermore, there is currently a lack of a clear model of how sensor-based worker assistance systems for data acquisition and analytics can be designed and systematically implemented. In the present research, a model for a vision-based worker assistance system for assembly was developed based on an industrial case study regarding a manual assembly line. The proposed model consists of five integrated modules: data acquisition, data preprocessing, data storage, data analysis, and simulation. The data acquisition module was constructed in the assembly workstation of the production line by implementing a depth camera, which together with an algorithm developed in Python for preprocessing, tracks the activities of the operator and inserts the processing times into a SQL table of the data storage module. This module contains all the relevant information of the production system, from the shop floor to the Manufacturing Execution System, enabling vertical integration. The data analysis module, aimed at the streaming and predictive analytics, was deployed in the RStudio platform. Likewise, the simulation module was conceptualized to retrieve real-time data from the shop floor and to select the best strategy. To evaluate the model testing of the proposed system in real production was performed. The results of this use case provide useful information for academia as well as practitioners how to implement vision-based worker assistance systems.
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