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

TL;DR

This paper analyzes seven years of sensor data from the ASTRI-Horn telescope to identify patterns and correlations, aiming to develop predictive maintenance strategies that prevent downtime in telescope operations.

Contribution

It presents a comprehensive data analysis approach for telescope maintenance, highlighting potential for predictive models based on historical sensor data.

Findings

Identification of key data patterns and correlations

Insights for developing predictive maintenance models

Potential reduction in telescope downtime

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.