Speaker
Description
Cherenkov telescope arrays are equipped with a multitude of sensors
spread all over the instrumentation and collect a large volume of
housekeeping and auxiliary data coming from telescopes, weather
stations and other devices in the array site.
In this poster we will present how we intend to exploit the sensor's
information, together with the most advanced artificial intelligence
algorithms, to perform predictive maintenance (PdM). This technique will
be useful to detect in advance the remaining useful life of the array
components, and to estimate the correct timing for performing their
maintenance. The application of PdM will allow to minimize the array
downtime, to increase the telescopes sub-components longevity, and to
reduce the costs due to unforeseen maintenance. Our model will be
trained and tested with time series data coming from a number of
different sensors (temperature, current, torque, etc.) dedicated to
monitor several mechanical components of the telescopes (engines,
cameras, encoders, etc.). The adopted supervised machine learning
approach will allow us to perform the correct trade-off between
preventive and corrective maintenance.
| Main Topic | Data mining |
|---|---|
| Participation mode | In person |
