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Related Experiment Video

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Data Communication Based on MQTT in a Polymer Extrusion Process

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Published on: July 15, 2022

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

Borja Bordel¹, Ramón Alcarria², Tomás Robles¹

¹Information Systems Department, Information Systems School, Campus Sur, Universidad Politécnica de Madrid, 28031 Madrid, Spain.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

This study introduces a novel predictor-corrector algorithm to enhance data quality for Industry 4.0 systems. The solution improves real-time data series, reducing errors and ensuring reliable decision-making in smart manufacturing.

Keywords:

Industry 4.0 data series interpolation techniques predictor-corrector algorithms sensor nodes

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Last Updated: Oct 13, 2025

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Published on: October 6, 2020

Area of Science:

Industrial Engineering
Computer Science
Data Science

Background:

Industry 4.0 integration relies on real-time data for efficiency but faces challenges with sensor data quality.
Sensor malfunctions (delays, errors, inactivity) pose critical risks to automated decision-making in Industry 4.0.
Current solutions for data integrity often increase deployment costs and limit Industry 4.0's transformative potential.

Purpose of the Study:

To develop a robust solution ensuring sensor data interoperability and quality for Industry 4.0 software.
To address the sensitivity of Industry 4.0 algorithms to imperfect sensor data.
To propose a cost-effective method for enhancing data reliability without compromising Industry 4.0's impact.

Main Methods:

A predictor-corrector numerical algorithm architecture was developed.
Techniques including Lagrange polynomial and Hermite interpolation were employed to adapt data series.
The method allows for expansion, contraction, and completion of data series using predicted and corrected samples.

Main Results:

The proposed solution operates in real-time, suitable for dynamic Industry 4.0 environments.
Demonstrated significant improvement in the quality of data series.
Effectively reduced the probability of errors in Industry 4.0 systems.

Conclusions:

The predictor-corrector approach successfully enhances sensor data quality for Industry 4.0 applications.
This method ensures better interoperability between sensors and software, crucial for Industry 4.0.
The solution contributes to more reliable and efficient smart manufacturing systems by mitigating data-related risks.