Prognostics CoE Publications

In this paper, we present an architecture and a formal framework to be used for systematic benchmarking of
monitoring and diagnostic systems and for producing comparable performance assessments of different diagnostic
technologies. The framework defines a number of standardized specifications, which include a fault catalog, a library of modular test scenarios, and a common protocol for gathering and processing diagnostic data. At the center of the framework are 13
benchmarking metric definitions. The calculation of metrics is
illustrated on a probabilistic model-based diagnosis algorithm
utilizing Bayesian reasoning techniques. The diagnosed system is
a real-world electrical power system, namely the Advanced
Diagnostics and Prognostics Testbed (ADAPT) developed and
located at the NASA Ames Research Center. The proposed
benchmarking approach shows how to generate realistic
diagnostic data sets for large-scale, complex engineering systems,
and how the generated experimental data can be used to enable
“apples to apples” assessments of the effectiveness of different
diagnostic and monitoring algorithms.