Abstract
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.
Highlights
The strengthening of important global crises, such as the climatic crisis or the natural resource crisis, makes essential a change in the productive schemes of all countries, but especially in those with a relevant industrial sector [1]
As can systems up to 10 sensor nodes, the proposed solution was able to curate all data series in systems up to sensor nodes, the proposed solution was able to curate all data series in real time, as the computational time was below the sampling period
In only one case was the computational time above the sampling period: for networks with 100 sensor nodes
Summary
The strengthening of important global crises, such as the climatic crisis or the natural resource crisis, makes essential a change in the productive schemes of all countries, but especially in those with a relevant industrial sector [1]. Contrary to other proposals (based, for example, on Gaussian distributions), this scheme is not dependent on the sensor technology or the software modules to be integrated, and it introduces (as shown in Section 4) a negligible delay, so real-time operation is not affected (something essential in Industry 4.0 systems). Some proposals define models to fill the gap between low-level infrastructures and data analytics components [26], while other schemes integrate semantic web components and ontologies to connect cyber-physical systems and knowledge management modules [27] Among all these solutions, digital twinning is the most promising approach [28]. Computationally heavy schemes to compensate different effects (such as redundant data) based on previous observations and offline processing may be found [47] All these proposals do not enable sensor interoperability (they are totally application-specific); on the contrary, they make it difficult. All kinds of malfunctions can be curated, and up to four different potential curated data series are analyzed before selecting the most probable one
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