Retrofitting for brownfield systems

Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0

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.

Post date: 03 January 2023

Ruppert, T., Darányi, A., Medvegy, T., Csereklei, D., & Abonyi, J. (2022). Demonstration Laboratory of Industry 4.0 Retrofitting and Operator 4.0 Solutions: Education towards Industry 5.0. Sensors, 23(1), 283.

Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation

A method for flexible vibration sensor-based retrofitting of CNC machines is proposed. As different states leave different fingerprints in the power spectrum plane, the states of the machine can be distinguished based on the features extracted from the spectrum map. Due to some states, like tool replacement, are less frequent than others, like production state, monitoring the machine states is considered an imbalanced classification problem. The key idea is to use Borderline-Synthetic Minority Oversampling Technique (Borderline-SMOTE) to augment the data set. The concept is validated in an industrial case study. Soft sensors based on four machine learning algorithms with and without SMOTE to predict the states of the machine were implemented. The results show that the SMOTE-based data augmentation improved the performance of the models by 50%.

Post date: 21 November 2022

Kummer, A., Ruppert, T., Medvegy, T., & Abonyi, J. (2022). Machine learning-based software sensors for machine state monitoring - The role of SMOTE-based data augmentation. Results in Engineering, 16, 2022

Retrofitting-based development of brownfield Industry 4.0 and Industry 5.0 solutions

The ongoing Industry 4.0 is characterized by the connectivity between components in the manufacturing system. For modern machines, the Internet of Things is a built-in function. In contrast, there are legacy machines in deployment functioning without digital communication. The need to connect them became popular to improve overall production efficiency. As building a new smart factory as a greenfield investment is a capital-intensive choice, retrofitting the existing infrastructure with IoT capability is more reasonable than replacing them. However, this so-called brownfield development, or retrofitting, requires specific prerequisites, e.g., digitization status assessment, technical and connectivity development, management requirement, and operational need, representing a significant disadvantage: lack of scalability. In the meantime, Industry 5.0 is under human-centric priority, which poses new challenges to the retrofitted system. Aware of the challenge, this paper provides a systematic overview of brownfield development regarding technical difficulties, supporting technologies, and possible applications for the legacy system. The research scope focuses on available Industry 4.0 advancements but considers preparing for the forthcoming Industry 5.0. The proposed retrofitting project approach can be a guideline for manufacturers to transform their factories into intelligent spaces with minimal cost and effort but still gain the most applicable solution for management needs. The future direction for other research in brownfield development for Industry 5.0 is also discussed.

T. -a. Tran, T. Ruppert, G. Eigner and J. Abonyi, "Retrofitting-based development of brownfield Industry 4.0 and Industry 5.0 solutions," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3182491.

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