Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of the Time of Flight Scintillation Array
This work addresses real-time diagnostic challenges for large-scale sensor systems, such as nuclear physics detectors, but is incremental as it builds on existing monitoring and diagnostic methods.
The authors tackled the problem of monitoring and diagnosing large-scale sensor-based systems with real-time constraints by developing a multilevel architecture that integrates statistical monitoring and model-based diagnostics, applying it to a nuclear physics detector with 5000 components and over 500 data channels, and demonstrating scalability for more complex systems.
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.