Detecting outliers and anomalies to prevent failures and accidents in Industry 4.0

Sik, David; Levendovszky, János

doi:10.1109/coginfocom50765.2020.9237903

Cited by 2 publications

(2 citation statements)

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“…Using different techniques, datasets are transformed, and anomalous data are removed. Techniques based on digital encoders [48], machine learning [49], statistical indicators [50], performance indicators [16] or hybrid approaches [51] have been described. Although these schemes are useful, they cannot be employed in real time, and many other potential malfunctions, such as packet losses, cannot be addressed through these solutions.…”

Section: State Of the Art On Sensor Interoperability In Industry 40 Scenariosmentioning

confidence: 99%

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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: State Of the Art On Sensor Interoperability In Industry 40 Scenariosmentioning

confidence: 99%

“…The convolution of these two Gaussian distributions is a third Gaussian distribution (50). The mean value m and the standard deviation s depend on the scenario and Industry 4.0 system, but most modern 5G solutions establish the mean value m around 1 millisecond and the standard deviation s is one magnitude order lower.…”

Section: Malfunction Modelingmentioning

confidence: 99%