Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Bordel, Borja; Alcarria, Ramón; Robles, Tomás

doi:10.15388/20-infor441

Cited by 3 publications

(1 citation statement)

References 65 publications

(70 reference statements)

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“…Using the sensor data, high-level software modules may create models to represent (and later predict) the production processes' and industry context's evolution, make realtime business decisions and (even) trigger alarms about workers' safety and wellbeing [10]. Some of these activities are critical in industry, and a precise evaluation and stable, highquality, physical monitoring are essential to avoid fatal problems.…”

Section: Introductionmentioning

confidence: 99%

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

Bordel

2021

Sensors

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Industry 4.0 is envisioned to transform the entire economical ecosystem by the inclusion of new paradigms, such as cyber-physical systems or artificial intelligence, into the production systems and solutions. One of the main benefits of this revolution is the increase in the production systems’ efficiency, thanks to real-time algorithms and automatic decision-making mechanisms. However, at the software level, these innovative algorithms are very sensitive to the quality of received data. Common malfunctions in sensor nodes, such as delays, numerical errors, corrupted data or inactivity periods, may cause a critical problem if an inadequate decision is made based on those data. Many systems remove this risk by seamlessly integrating the sensor nodes and the high-level components, but this situation substantially reduces the impact of the Industry 4.0 paradigm and increases its deployment cost. Therefore, new solutions that guarantee the interoperability of all sensors with the software elements in Industry 4.0 solutions are needed. In this paper, we propose a solution based on numerical algorithms following a predictor-corrector architecture. Using a combination of techniques, such as Lagrange polynomial and Hermite interpolation, data series may be adapted to the requirements of Industry 4.0 software algorithms. Series may be expanded, contracted or completed using predicted samples, which are later updated and corrected using the real information (if received). Results show the proposed solution works in real time, increases the quality of data series in a relevant way and reduces the error probability in Industry 4.0 systems.

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Section: Introductionmentioning

confidence: 99%

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

Bordel

2021

Sensors

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Increasing the Efficiency and Workers Wellbeing in the European Bakery Industry: An Industry 4.0 Case Study

Bordel

Torre²,

Carretero³

2022

Lecture Notes in Networks and Systems

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Industry 4.0 refers to a revolution where innovative digital instruments will be employed to improve the production process efficiency as well as the working conditions. However, this new approach is not the only way to achieve those critical objectives, and many companies implemented years ago quality management plans guaranteeing both, the production efficiency, and workers wellbeing. The nature of these solutions and their associated costs is totally different, and while Industry 4.0 schemes need important deployment costs, traditional management plans are supported by human resources. Companies should restructure, then. Thus, the Industry 4.0 adoption challenge is not necessarily technological but economical and strategic. And clear quantitative analyses showing the advantages (economical, societal, …) at short and long term are required to promote this revolution. In this paper we present a study case focused on the European bakery industry, where a sensing platform is employed to increase the global production process efficiency and the workers wellbeing. Variables such as temperature, humidity, power consumption and air quality are monitored through sensor nodes. A framework of performance and economic indicators is generated, and we analyze their changes and envisioned evolution after the Industry 4.0 paradigm adoption. Results show the advantages of these new solutions in an objective and clear way.

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Empowering European Customers: A Digital Ecosystem for Farm-to-Fork Traceability

Bordel

Alcarria

Torre³

et al. 2023

Lecture Notes in Networks and Systems

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Controlling Supervised Industry 4.0 Processes through Logic Rules and Tensor Deformation Functions

Cited by 3 publications

References 65 publications

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

Increasing the Efficiency and Workers Wellbeing in the European Bakery Industry: An Industry 4.0 Case Study

Empowering European Customers: A Digital Ecosystem for Farm-to-Fork Traceability

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