One of the main challenges of Industry 4.0 is how advanced sensors and sensing technologies can be applied through the Internet of Things layers of existing manufacturing. This is the so-called Brownfield Industry 4.0, where the different types and ages of machines and processes need to be digitalized. Smart retrofitting is the umbrella term for solutions to show how we can digitalize manufacturing machines. This problem is critical in the case of solutions to support human workers. The Operator 4.0 concept shows how we can efficiently support workers on the shop floor. The key indicator is the readiness level of a company, and the main bottleneck is the technical knowledge of the employees. This study proposes an education framework and a related Operator 4.0 laboratory that prepares students for the development and application of Industry 5.0 technologies. The concept of intelligent space is proposed as a basis of the educational framework, which can solve the problem of monitoring the stochastic nature of operators in production processes. The components of the intelligent space are detailed through the layers of the IoT in the form of a case study conducted at the laboratory. The applicability of indoor positioning systems is described with the integration of machine-, operator- and environment-based sensor data to obtain real-time information from the shop floor. The digital twin of the laboratory is developed in a discrete event simulator, which integrates the data from the shop floor and can control the production based on the simulation results. The presented framework can be utilized to design education for the generation of Industry 5.0.

This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.

Wire harness assembly is a labor-intensive yet critical process in the automotive industry,requiring efficient and well-coordinated workflows. The assembly is carried out on boardswhere workers route wires through fixtures. On larger boards, hundreds of wires areassembled, often requiring multiple operators to work together. In this case, their work mustbe coordinated so that they can collaborate effectively without interfering with each other.This research focuses on optimizing task allocation to minimize total assembly time inmulti-agent settings. Optimization considers individual performance differences betweenworkers, as well as spatial factors—assembly tasks are generally faster in central areas andslower at the periphery. Furthermore, it distinguishes between operation types, such as thefaster task of switching between wires versus the slower process of laying a wire along theboard.To solve this problem, we developed a genetic algorithm-based task assignmentoptimization algorithm that is applicable to any agent. The case study was based on a realassembly board with a custom layout, and we also created a visualization simulation andperformed a lab demonstration.