The Accelerator Operations and Technology division at Los Alamos Neutron Science Center (LANSCE) is working on designing and implementing an Instrumentation and Controls System (ICS) for the Cathodes and Radio-frequency Interactions in Extremes (CARIE) project. The system will utilize open-source Experimental Physics and Industrial Control System (EPICS) developed for scientific facilities for control, monitoring, and data acquisition. The hardware form factors will include National Instrument’s (NI) cRIO automation controller for industrial-like slow inputs/outputs and NI’s PXIe for high-speed data acquisition for diagnostic signals featuring masked and event-based time window capture. In this paper, we will discuss the reasons that led to the design, the hardware and software design specifics, the challenges that we faced during implementation, including the EPICS device support for NI PXIe, as well as the advantages and drawbacks of our system given the experimental nature of the CARIE project.

Particle accelerators are among the largest and most expensive scientific facilities. Constant monitoring of data from a diverse array of diagnostics is imperative to ensure proper operational parameters—such as beam parameters, power sources, cooling systems, etc. Detecting equipment failure within this data stream is challenging due to the accelerator parameters gradually shifting over time due to diverse user demands, environmental factors, and the feedback control system's operation. At LANSCE, identifying anomalies stemming from deteriorating equipment is a significant issue. To address this, we propose implementing an anomaly detection system based on existing machine learning algorithms. This system will monitor all available data for each accelerator subsystem, establish typical parameter ranges, and determine whether the measured parameters fall beyond those thresholds. This anomaly detection system aims to factor in intrinsic internal correlations among various parameters, which the current Data Watcher warning system fails to consider. We anticipate that this developed warning system will effectively identify ongoing equipment degradation and predict upcoming failures.