{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:17:44Z","timestamp":1781018264491,"version":"3.54.1"},"reference-count":11,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:00:00Z","timestamp":1750291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/data10060091","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T09:57:48Z","timestamp":1750327068000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sensor-Based Monitoring Data from an Industrial System of Centrifugal Pumps"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1837-1734","authenticated-orcid":false,"given":"Angelo","family":"Martone","sequence":"first","affiliation":[{"name":"Laboratory of Knowledge Management and Digital Resources, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6035-1537","authenticated-orcid":false,"given":"Alessia","family":"D\u2019Ambrosio","sequence":"additional","affiliation":[{"name":"Department of Physics Ettore Pancini, University of Naples Federico II, 80125 Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7524-4339","authenticated-orcid":false,"given":"Michele","family":"Ferrucci","sequence":"additional","affiliation":[{"name":"Laboratory of Knowledge Management and Digital Resources, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0043-0356","authenticated-orcid":false,"given":"Assuntina","family":"Cembalo","sequence":"additional","affiliation":[{"name":"Laboratory of Knowledge Management and Digital Resources, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1632-7163","authenticated-orcid":false,"given":"Gianpaolo","family":"Romano","sequence":"additional","affiliation":[{"name":"Unit of Digital Data Management, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6042-6650","authenticated-orcid":false,"given":"Gaetano","family":"Zazzaro","sequence":"additional","affiliation":[{"name":"Laboratory of Data Science for Research Facilities, CIRA (Italian Aerospace Research Centre), 81043 Capua, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mobley, R.K. (2004). 1\u2014Impact of Maintenance. Maintenance Fundamentals, Plant Engineering, Butterworth-Heinemann. [2nd ed.].","DOI":"10.1016\/B978-075067798-1\/50022-4"},{"key":"ref_2","unstructured":"(2025, June 13). NASA Bearing Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/vinayak123tyagi\/bearing-dataset."},{"key":"ref_3","unstructured":"(2025, June 13). MFPT Bearing Fault Dataset. Available online: https:\/\/www.mfpt.org\/fault-data-sets."},{"key":"ref_4","unstructured":"(2025, June 13). UCI Accelerometer Datase. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets\/accelerometer."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1007\/s10845-023-02151-y","article-title":"Early fault diagnosis in rolling element bearings: Comparative analysis of a knowledge-based and a data-driven approach","volume":"35","author":"Iunusova","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"ref_6","unstructured":"(2025, June 13). Telegraf Website. Available online: https:\/\/www.influxdata.com\/time-series-platform\/telegraf."},{"key":"ref_7","unstructured":"(2025, June 13). Modbus Website. Available online: https:\/\/www.modbus.org\/."},{"key":"ref_8","unstructured":"(2025, June 13). InfluxDB Website. Available online: http:\/\/influxdata.com\/."},{"key":"ref_9","unstructured":"(2025, June 13). Grafana website. Available online: https:\/\/grafana.com\/."},{"key":"ref_10","first-page":"1","article-title":"Harnessing Industry 4.0 Technologies: A Novel Predictive Maintenance Method for Advanced Production Systems","volume":"15","author":"Bednarek","year":"2025","journal-title":"Manag. Prod. Eng. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rojas, L., Pe\u00f1a, A., and Garcia, J. (2025). AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management. Appl. Sci., 15.","DOI":"10.3390\/app15063337"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/6\/91\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:55:11Z","timestamp":1760032511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/6\/91"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,19]]},"references-count":11,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["data10060091"],"URL":"https:\/\/doi.org\/10.3390\/data10060091","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,19]]}}}