Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of the Time of Flight Scintillation Array

TL;DR

This paper introduces a scalable, multi-level architecture for real-time monitoring and diagnosis of large sensor-based systems, effectively managing uncertainties across different diagnostic levels.

Contribution

It proposes a novel multi-level architecture combining statistical and model-based methods for uncertainty management in large-scale sensor systems.

Findings

Successfully applied to a nuclear physics detector with 5000 components

Demonstrated scalability to systems 10-100 times more complex

Integrated uncertainty management improves diagnostic accuracy

Abstract

We present a general architecture for the monitoring and diagnosis of large scale sensor-based systems with real time diagnostic constraints. This architecture is multileveled, combining a single monitoring level based on statistical methods with two model based diagnostic levels. At each level, sources of uncertainty are identified, and integrated methodologies for uncertainty management are developed. The general architecture was applied to the monitoring and diagnosis of a specific nuclear physics detector at Lawrence Berkeley National Laboratory that contained approximately 5000 components and produced over 500 channels of output data. The general architecture is scalable, and work is ongoing to apply it to detector systems one and two orders of magnitude more complex.