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Authors:
Martin Zinner
Hajo Wiemer
Kim Feldhoff
Peter Meschke
Steffen Ihlenfeldt
Keywords: Failure analysis; Sensor data quality; Sensor data error detection.
Abstract:
Cyber Physical Production Systems (CPPS) depend significantly on high-quality sensor data to function optimally, make decisions in real-time, and perform predictive maintenance inter alia. Nevertheless, the quality of sensor data in industrial settings is often affected by various factors such as environmental interference, hardware wear and tear, calibration drift, and intricate system interactions. This study introduces innovative methods to improve sensor data quality in CPPS through systematic digitalization strategies. By employing a use case methodology, we explore real-world production scenarios to pinpoint common data quality challenges and devise specific solutions. Our strategy integrates signal processing techniques, algorithms for detecting anomalies to establish robust frameworks for data validation and correction. The proposed methods offer practical, scalable solutions that can be adapted to various production environments, thereby enhancing the reliability and efficiency of cyber physical manufacturing systems. To illustrate the feasibility of our approach, we utilise the case study of a test bed.
Pages: 55 to 67
Copyright: Copyright (c) IARIA, 2025
Publication date: September 28, 2025
Published in: conference
ISSN: 2519-8599
ISBN: 978-1-68558-295-1
Location: Lisbon, Portugal
Dates: from September 28, 2025 to October 2, 2025