The Cherenkov Telescope Array Observatory (CTAO) embodies the next phase of ground-based gamma-ray astronomy, engineered to function in the age of multimessenger astronomy. This observatory consists of two arrays, accommodating a collective count of more than 60 Cherenkov telescopes. These telescopes are strategically positioned in both the Northern hemisphere on La Palma Island, Spain, and the Southern hemisphere at Paranal, Chile. CTAO integrates a diverse array of telescope designs and scientific instruments, all collaboratively working to achieve unmatched sensitivity and energy coverage. This collective effort aims to advance the exploration of transient phenomena within the GeV-TeV range. This paper delineates the ongoing development of the monitoring, logging, and alarm subsystems within the Array Control and Data Acquisition System (ACADA) for the CTAO. The Monitoring System (MON) is tasked with overseeing and logging the overall conditions of the array. It has the capability to acquire the fundamental data required to enable predictive maintenance to minimize system downtime. The MON provides an unified tool for monitoring data items from telescopes and calibration instruments at CTAO sites, ensuring immediate availability for operators and facilitating quick-look quality checks. Meanwhile, the Array Alarm System (AAS) collects, filters, and exposes alarms originating from ACADA processes and array elements, thereby enhancing observational efficiency. This paper outlines the MON and AAS, including the technological implementation choices.
KEYWORDS: Telescopes, Atmospheric Cherenkov telescopes, Environmental monitoring, Matrices, Temperature metrology, Principal component analysis, Internet of things, Windows, Visualization, Sensors
This research analyzes historical data from the ASTRI-Horn, a Cherenkov telescope at the Astrophysical Observatory of Catania (Serra La Nave, Mt. Etna). Data from a multitude of sensors, distributed across the telescope, were studied. These sensors record various parameters, including currents, voltages, phases, positions, and temperatures, from different telescope components such as motors and encoders, as well as environmental conditions like temperature and humidity. Seven years of operational data have been analyzed to identify precursors indicative of component degradation. The aim was to discern unique data patterns or "signatures" corresponding with periods of component damage or replacement. These identified signatures will be instrumental in the development of a Predictive Maintenance (PdM) model, which will aim to foresee the standard operational patterns, issuing alerts for any detected anomalies or deviations, thereby facilitating early anomaly detection and resolution. PdM is an advanced maintenance strategy that uses data to help predict when parts might fail aiming to reduce unexpected costs, improving the overall efficiency and reliability of the telescope.
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