New Conference Paper Published

We are excited to announce that our conference paper titled “A Data Fusion Service-Oriented Infrastructure for Production Line Monitoring” has been published in the proceedings of the 2024 IEEE International Conference on Industrial Technology (ICIT).

Abstract

The Industry 4.0 paradigm has deeply changed classical manufacturing by introducing data-based analytics and decision-support strategies. At the state of the art, data used for manufacturing monitoring is mostly originated by sensors, that undergo a fusion step to align different data sources. However, this data is only relative to the monitored process, and it does not include the corresponding operating conditions and parameters, that are known by the Manufacturing Execution System (MES). Such information is currently either not included or labeled by hand, thus incurring in errors and limiting the amount of available labeled data. To overcome this issue and go beyond the sole data fusion of sensor data, this paper proposes an infrastructure that automatically labels time series generated by sensors with information extracted from the MES, to achieve enhanced monitoring of the production process. The relevance of the proposed solution and the possibilities opened by its application are stressed with the application to a robotic arm.

Details

• Title: A Data Fusion Service-Oriented Infrastructure for Production Line Monitoring
• Authors: Sebastiano Gaiardelli, Nicola Dall’Ora, Francesco Ponzio, Enrico Fraccaroli, Franco Fummi, Santa Di Cataldo, Sara Vinco
• Conference: IEEE International Conference on Industrial Technology (ICIT)
• Year: 2024
• Pages: 1-8
• Keywords: Data analysis, Time series analysis, Data integration, Production, Sensor fusion, Robot sensing systems, Manipulators, Industry 4.0, Industrial IoT sensors, Data fusion, Process monitoring, Anomaly detection

Links

• DOI: 10.1109/ICIT58233.2024.10541026
• Open Access Version: Read Here

This paper presents a novel infrastructure for enhancing production line monitoring by combining sensor data with information from the Manufacturing Execution System (MES). By automatically labeling time series data with MES information, the proposed solution reduces manual errors and increases the availability of labeled data for process monitoring and anomaly detection. Its effectiveness is demonstrated with a case study involving a robotic arm.

We thank our co-authors and collaborators for their contributions to this work. For further details, feel free to explore the full paper through the links provided above.