Sensor-Based Monitoring Data from an Industrial System of Centrifugal Pumps

Angelo Martone, Alessia D’Ambrosio, Michele Ferrucci, Assuntina Cembalo, Gianpaolo Romano and Gaetano Zazzaro ()
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Angelo Martone: Laboratory of Knowledge Management and Digital Resources, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy
Alessia D’Ambrosio: Department of Physics Ettore Pancini, University of Naples Federico II, 80125 Napoli, Italy
Michele Ferrucci: Laboratory of Knowledge Management and Digital Resources, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy
Assuntina Cembalo: Laboratory of Knowledge Management and Digital Resources, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy
Gianpaolo Romano: Unit of Digital Data Management, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy
Gaetano Zazzaro: Laboratory of Data Science for Research Facilities, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy

Data, 2025, vol. 10, issue 6, 1-11

Abstract: We present a detailed dataset collected via a wireless IoT sensor network monitoring three industrial centrifugal pumps (units A, B, and C) at the Italian Aerospace Research Centre (CIRA), along with the methods for data collection and structuring. Background : Centrifugal pumps are critical in industrial plants, and monitoring their condition is essential to ensure reliability, safety, and efficiency. High-quality operational data under normal operating conditions are fundamental for developing effective maintenance strategies and diagnostic models. Methods : Data were gathered by means of smart sensors measuring motor and pump vibrations, temperatures, outlet fluid pressures, and environmental conditions. Data were transmitted over a WirelessHART mesh network and acquired through an IoT architecture. Results : The dataset consists of eight CSV files, each representing a specific pump during a distinct operational day. Each file includes timestamped measurements of displacement, peak vibration values, sensor temperatures, fluid pressure, ambient temperature, and atmospheric pressure. Conclusions : This dataset supports advanced methodologies in feature extraction, multivariate signal analysis, unsupervised pattern discovery, vibration analysis, and the development of digital twins and soft sensing models for predictive maintenance optimization.

Keywords: centrifugal pumps; condition monitoring; IoT sensors; industrial maintenance data; statistical data analysis (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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