OSPtk: Cost-aware Optimal Sensor Placement Toolkit Enabling Design-for-PHM in Critical Industrial Systems

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Published Oct 26, 2023
Liang Tang Abhinav Saxena Scott Evans Naresh Iyer Helena Goldfarb

Abstract

Performance of a Prognostics and Health Management (PHM) system in a fielded application depends on observability from existing monitoring equipment and sensing, which get determined at the design phase. Although various technologies have been proposed in the literature, there is currently a lack of known generic tools specifically designed for performing design stage sensor placement analyses from a PHM perspective. This leads to PHM observability being an afterthought and resulting PHM designs being sub-optimal. This paper describes a new Optimal Sensor Placement (OSP) framework, its implementation as a toolkit and the experience with applying it to a new product design in the context of a Small Modular Reactor (SMR). The formulation adds multiple important features that are critical to PHM applications. Firstly, it establishes a direct link to PHM performance requirements with intent to reduce operational and maintenance costs. Moreover, it acknowledges and accounts for the costs and risks of errors that PHM system will incur, and simultaneously considers operational requirements on sensing for performance, control and/or regulatory requirements. The toolkit described here implements formulations of a large number of requirements scenarios applicable in a generic industrial product development setting.

How to Cite

Tang, L., Saxena, A., Evans, S., Iyer, N., & Goldfarb, H. (2023). OSPtk: Cost-aware Optimal Sensor Placement Toolkit Enabling Design-for-PHM in Critical Industrial Systems. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3557
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Keywords

Optimal sensor placement, sensor selection, design for phm, machine learning, small modular reactor, information fusion, prognostics and health management

References
Blum, C., Puchinger, J., Raidl, G. R., Roli, A., Hybrid metaheuristics in combinatorial optimization: A survey, Applied Soft Computing, Volume 11, Issue 6, 2011, Pages 4135-4151.

Bodden, D. S., Hadden, W., Grube, B. E. and Clements, N. S., 2005. PHM as a Design Variable in Air Vehicle Conceptual Design, 2005 IEEE Aerospace Conference, Big Sky, MT, USA, 2005, pp. 1-11, doi: 10.1109/AERO.2005.1559640.

Jones, P.M.; Lonne, Q.; Talaia, P.; Leighton, G.J.T.; Botte, G.G.; Mutnuri, S.; Williams, L. A Straightforward Route to Sensor Selection for IoT Systems. Res. Technol. Manag. 2018, 61, 41–50.

Kertiou, I.; Benharzallah, S.; Kahloul, L.; Beggas, M.; Euler, R.; Laouid, A.; Bounceur, A. A dynamic skyline technique for a context-aware selection of the best sensors in an IoT architecture. Ad Hoc Netw. 2018, 81, 183–196.

Kulkarni, A., Terpenny, J. and Prabhu, V., 2021. Sensor selection framework for designing fault diagnostics system. Sensors, 21(19), p.6470.

Kurtoglu, T., Johnson, S., Barszcz, E., Johnson, J., and Robinson, P., 2008. Integrating system health management into the early design of aerospace systems using Functional Fault Analysis. 1-11. 10.1109/PHM.2008.4711425.

MIL-STD-2165, Testability Program for Electronic Systems and Equipments, 26 January 1985.

Mitchell, M., 1996. An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press. ISBN 9780585030944.

Riedel, M.; Arroyo, E.; Fay, A. Knowledge-based selection of principle solutions for sensors and actuators based on standardized plant description and semantic concepts. In Proceedings of the 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), Luxembourg, 8–11 September 2015; pp. 1–8.

Schmidt, A.; van Laerhoven, K. How to build smart appliances? IEEE Pers. Commun. 2001, 8, 66–71.

Shieh, J.; Huber, J.E.; Fleck, N.A.; Ashby, M.F. The selection of sensors. Prog. Mater. Sci. 2001, 46, 461–504.

Singh, S., Holland, S.W., Bandyopadhyay, P.: Trends in the development of system-level fault dependency matrices. In Proceedings of IEEE Aerospace Conference, pp. 1–9 (2010)

Thombare, T.R.; Dole, L. D-Matrix: Fault Diagnosis Framework. Int. J. Innov. Res. Comput. Commun. Eng. 2015, 3, 1740–1745.

Tjen, J.; Smarra, F.; D’Innocenzo, A. An entropy-based sensor selection algorithm for structural damage detection. In Proceedings of the 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Virtual. 20–21 August 2020; pp. 1566–1571.

Zhang, G.; Vachtsevanos, G. A Methodology for Optimum Sensor Localization/Selection in Fault Diagnosis. In Proceedings of the 2007 IEEE Aerospace Conference, Big Sky, MT, USA, 3–10 March 2007.
Section
Industry Experience Papers

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