Robust sensor network design for fault diagnosis

An appropriately designed sensor network is crucial for the Success of any fault diagnostic strategy. Strategies for optimally locating sensors based on reliability-maximization and cost-minimization exist. While reliability and cost criteria have been used in selecting optimal sensor networks, the robustness of the network to uncertainties/errors in the underlying model and probability data have not been considered. In this article. we present robustness enhancing criteria for the design of sensor networks for reliable fault diagnosis. Robustness to modeling errors is incorporated by choosing a distributed sensor network. Robustness to available probability data is incorporated by minimizing the unreliability of fault detection with uncertain probability data. These criteria are incorporated in a lexicographic manner along with the overall reliability-maximization and cost-minimization objectives in a single optimization problem. Solution to this optimization problem results in a solution that is most reliable, robust and cost minimal in a lexicographic sense. The utility of the proposed approaches is demonstrated through the Tennessee Eastman case study. (C) 2007 Elsevier Ltd. All rights reserved.