Prognostics and diagnostics of conflicts and errors with prevention and detection logic

Xin Chen, Purdue University

Abstract

The objective of this research is to design and develop effective methods to prevent and detect conflicts and errors (CEs) through prognostics and diagnostics. The Centralized Conflict and Error Prevention and Detection (CEPD) Logic and Decentralized CEPD Logic are developed for prognostics and diagnostics over three types of real-world constraint networks: random network (RN), scale-free network (SFN), and Bose-Einstein condensation network (BECN). The CEPD logic is compared with the Traditional CEPD Algorithm using four performance measures: total CEPD time (TT), CE coverage ability (CA), CE prognostics ability (PA), and total damage (TD ). Analytical and experiment results show that (1) compared to the Centralized Logic and Traditional Algorithm, the Decentralized Logic is preferred and requires the least TT (80% less), obtains the largest CA and PA (two times larger), and minimizes TD (50% less); (2) if the Decentralized Logic cannot be applied for certain reasons, e.g., communication disruptions between distributed agents, the Network-Adaptive Centralized Logic should be used to obtain larger CA and PA (10 times larger) and smaller TD (at least 10% smaller) compared to the Traditional Algorithm.

Degree

Ph.D.

Advisors

Nof, Purdue University.

Subject Area

Systems science|Operations research

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