A Benchmark Diagnostic Model Generation System

• Published in: IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 40; no. 5; pp. 959 - 981
• Main Authors: Jun Wang, Provan, G
• Format: Journal Article
• Language: English
• Published: IEEE 01.09.2010
• Subjects: Algorithm design and analysis; Benchmark model generation; Biological system modeling; Circuit topology; Circuits and systems; compositional modeling; Costs; diagnosis; Graphical models; Network topology; Process control; Process design; Very large scale integration
• ISSN: 1083-4427; 1558-2426
• DOI: 10.1109/TSMCA.2010.2052039

It is critical to use automated generators for synthetic models and data given the sparsity of benchmark models for empirical analysis and the cost of generating models by hand. We describe an automated generator for benchmark models that is based on using a compositional modeling framework and employs graphical models for the system topology. We propose a three-step process for synthetic model generation: 1) domain analysis; 2) topology generation; and 3) system-level behavioral model generation. To demonstrate our approach on two highly different domains, we generate models using this process for circuits drawn from the International Symposium on Circuits and Systems benchmark suite and a process-control system. We then analyze the synthetic models according to two criteria: 1) topological fidelity and 2) diagnostic efficiency. Based on this comparison, we identify parameters necessary for the autogenerated models to generate benchmark diagnosis circuit and process-control models with realistic properties.