Predictive maintenance is a well studied collection of techniques that aims to prolong the life
of a mechanical system by using artificial intelligence and machine learning to predict the …

We present a multi-module framework based on Conditional Variational Autoencoder
(CVAE) to detect anomalies in the power signals coming from multiple High Voltage …

There exists three main areas of study inside of the field of predictive maintenance: anomaly
detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly detection …

This paper presents an in-depth analysis of multi-modal, deep learning-based frameworks
for fault detection within big research infrastructures, with a specific focus on synchrotron …

In Particle accelerators, commissioning of a complex beam line requires extensive use of
computer models. When the as-built beam line cannot be exactly modeled by the simulation …

High energy particle accelerators rely on superconducting radio frequency cavities to
transfer energy and accelerate the beam. Such particle accelerators are complex and …

Failures in large, complex systems can be difficult to diagnose and expensive for both the
system maintainers and users. We present techniques for predicting failures when there is …

The ALPI accelerator radio frequency (RF) control system at LNL (Legnaro National
Laboratories) is currently undergoing a series of upgrades which will extends its lifetime and …

Particle accelerators play a crucial role in scientific research and industrial applications, and
enhancing their reliability, ensuring stable operation, and reducing downtime caused by …

Particle accelerators are used all around the world for fundamental physics research,
medical diagnosis and industrial applications. These can be extremely complex machines …