Diagnosability-Based Sensor Placement through Structural Model Decomposition
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system.
How to Cite
##plugins.themes.bootstrap3.article.details##
structural model decomposition, sensor placement
Bregon, A., Daigle, M., Roychoudhury, I., Biswas, G., Koutsoukos, X., & Pulido, B. (2014, May). An event-based distributed diagnosis framework using structural model decomposition. Artificial Intelligence, 210, 1-35.
Casillas, M., Puig, V., Garza-Castañon, L., & Rosich, A. (2013). Optimal sensor placement for leak location in water distribution networks using genetic algorithms. Sensors, 13(11), 14984–15005. doi: 10.3390/s131114984
Daigle, M. (2008). A qualitative event-based approach to fault diagnosis of hybrid systems. Unpublished doctoral dissertation, Vanderbilt University.
Daigle, M., Bregon, A., Biswas, G., Koutsoukos, X., & Pulido, B. (2012, August). Improving multiple fault diagnosability using possible conflicts. In Proceedings of the 8th IFAC symposium on fault detection, supervision and safety of technical processes (p. 144-149).
Daigle, M., Koutsoukos, X., & Biswas, G. (2007, April). Distributed diagnosis in formations of mobile robots. IEEE Transactions on Robotics, 23(2), 353–369.
Debouk, R., Lafortune, S., & Teneketzis, D. (2002). On an optimization problem in sensor selection. Discrete Event Dynamic Systems, 12(4), 417–445.
Eriksson, D., Krysander, M., & Frisk, E. (2012, August). Using quantitative diagnosability analysis for optimal sensor placement. Mexico City, Mexico.
Frisk, E., Krysander, M., & A° slund, J. (2009). Sensor placement for fault isolation in linear differential-algebraic systems. Automatica, 45(2), 364-371.
Karnopp, D. C., Margolis, D. L., & Rosenberg, R. C. (2000). Systems dynamics: Modeling and simulation of mechatronic systems. New York: John Wiley & Sons, Inc.
Krysander, M., & Frisk, E. (2008). Sensor placement for fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 38(6), 1398–1410.
Mosterman, P. J., & Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 29(6), 554-565.
Narasimhan, S., Mosterman, P. J., & Biswas, G. (1998, May). A systematic analysis of measurement selection algorithms for fault isolation in dynamic systems. In Proc. of the 9th international workshop on principles of diagnosis (pp. 94–101). Cape Cod, MA USA.
Raghuraj, R., Bhushan, M., & Rengaswamy, R. (1999). Locating sensors in complex chemical plants based on fault diagnostic observability criteria. AIChE Journal, 45(2), 310–322.
Rosich, A. (2012). Sensor Placement for Fault Detection and Isolation based on Structural Models. In Proceedings of the 8th ifac symposium on fault detection, supervision and safety of technical processes, safeprocess12 (p. 391-396). Mexico City, Mexico.
Rosich, A., Frisk, E., Aslund, J., Sarrate, R., & Nejjari, F. (2012, March). Fault diagnosis based on causal computations. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 42(2), 371- 381.
Roychoudhury, I., Biswas, G., & Koutsoukos, X. (2009, April). Designing distributed diagnosers for complex continuous systems. IEEE Transactions on Automation Science and Engineering, 6(2), 277–290.
Roychoudhury, I., Daigle, M., Bregon, A., & Pulido, B. (2013, March). A structural model decomposition framework for systems health management. In Proceedings of the 2013 IEEE aerospace conference.
Said, A., & Djamel, B. (2013). Optimal sensor placement for fault detection and isolation by the structural adjacency matrix. International Journal of Physical Sciences, 8(6), 225–230.
Travé-Massuy`es, L., Escobet, T., & Olive, X. (2006, Nov). Diagnosability analysis based on component-supported analytical redundancy relations. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 36(6), 1146-1160.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.