$proj_team_count = count($proj_team); ?>

Error Detection and Error Localization

Machine Learning for System Health Management in radio astronomy

Approaches for Radio Telescope System Health Management

escience methodology

Error Detection and Error Localization

Error Detection and Error Localization

Principal Investigator
Dr. Albert-Jan Boonstra
ASTRON

Approaches for Radio Telescope System Health Management

In modern radio telescopes, System Health Management (SHM) systems are crucial for (early) detection of errors and for remedying them. Due to the increasing scale and complexity of the systems involved, the effectiveness and efficiency of current day SHM approaches are limited. Therefore, intelligent automated SHM approaches would significantly improve the quality and availability of the observational systems.

Intelligent automated approaches to improve the quality and availability of observational systems

Crucial for scientific results

This is not only beneficial for maintenance, operations, and cost. It also is crucial for the scientific results, as accurate knowledge of the state of the telescope is essential for calibrating the system. Data analytics and more specifically Machine Learning (ML) have shown to be able to “learn” from data.

The purpose of this project is to investigate the applicability of novel approaches such as ML for SHM in radio astronomy. Although this project focuses on application of this technology in radio astronomy, similar problems arise in scientific instruments across many disciplines, such as high-energy physics, ecology, life sciences and urban planning.

A generic methodology

Similar problems also occur in large-scale simulations, for example in water management, computational chemistry and climate science. In this alliance, a generic methodology will be developed which is also applicable in these fields.

Image: Afshin Darian - The eight radio telescopes of the Smithsonian Submillimeter Array, located at the Mauna Kea Observatory in Hawaii - https://en.wikipedia.org/wiki/Very-long-baseline_interferometry#/media/File:Smithsonian_Submillimeter_Array.jpg

Sign up for our newsletter