Accelerating astronomical applications 2
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.
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.