Revealing Predictive Maintenance Strategies from Comprehensive Data Analysis of ASTRI-Horn Historical Monitoring Data

Astronomical Society of the Pacific Conference Series 2026 Open access

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Authors: Federico Incardona, Alessandro Costa, G. Leto, Kevin Munari, Giovanni Pareschi, Salvatore Scuderi, Gino Tosti, the ASTRI project
DOI: 10.26624/mtnn7542
Cited by: 1

Abstract

Modern telescope facilities generate data from various sources, including sensors, weather stations, LiDARs, and FRAMs. Sophisticated software architectures using the Internet of Things (IoT) and big data technologies are required to manage this data. This study explores the potential of sensor data for innovative maintenance techniques, such as predictive maintenance (PdM), to prevent downtime that can affect research. We analyzed historical data from the ASTRI-Horn Cherenkov telescope, spanning seven years, examining data patterns and variable correlations. The findings offer insights for triggering predictive maintenance model development in telescope facilities.

Fields of Study & Subjects

Fault Detection and Control Systems

Publication details

Journal / source
Astronomical Society of the Pacific Conference Series
Publisher
Astronomical Society of the Pacific Conference Series
Volume
541
Pages
193–196
Publication date
28 February 2026
Language
EN