The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm de- pends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognos- tics algorithm is demonstrated as the prognostics algorithm moves up TRLs.
How to Cite
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.
Be ́rard, B., Bidoit, M., Finkel, A., Laroussinie, F., Petit, A., Petrucci, L., & Schnoebelen, P. (2010). Systems and software verification: Model-checking techniques and tools. Springer Publishing Company, Incorporated.
Byington, C. S., Roemer, M., Kalgren, P., & Vachtsevanos, G. (2005). Verification and validation of diagnos- tic/prognostic algorithms. In Machinery Failure Pre- vention Technology Conference.
Daigle, M., & Goebel, K. (2011). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2(2), 008.
Feather, M. S., & Markosian, L. Z. (2008). Towards certification of a space system application of fault detection and isolation. In Proceedings of the 2008 International Conference on Prognostics and health management (pp. 6–9).
Firesmith, D. (2003). Specifying good requirements. Journal of Object Technology, 2(4), 77–87.
Gupta, A. (1993). Formal hardware verification methods: A survey. In Computer-Aided Verification (pp. 5–92).
Hicks, B., Larsson, A., Culley, S., & Larsson, T. (2009). A methodology for evaluating technology readiness during product development. In Proceedings of the International Conference on Engineering Design (pp. 157– 168).
Kalos, M. H., & Whitlock, P. A. (2008). Monte carlo methods. John Wiley & Sons.
Mankins, J. C. (1995). Technology readiness levels. White Paper, April, 6.
McMillan, K. L. (2000). A methodology for hardware verification using compositional model checking. Science of Computer Programming, 37(1), 279–309.
Rajamani, R., Saxena, A., Kramer, F., Augustine, M., Schroeder, J. B., Goebel, K., . . . Lin, W. (2013).
Guidelines for writing ivhm requirements for aerospace systems. In Proceedings of the SAE 2013 AeroTech Congress & Exhibition.
Romero, R., Summers, H., & Cronkhite, J. (1996). Feasibility study of a rotorcraft health and usage monitoring system (hums): Results of operator’s evaluation. (Tech. Rep.). DTIC Document.
Roychoudhury, I., Hafiychuk, V., & Goebel, K. (2013). Model-based diagnosis and prognosis of a water recycling system. In IEEE Aerospace Conference (pp. 1– 9).
Saha, B., Koshimoto, E., Quach, C. C., Hogge, E. F., Strom, T. H., Hill, B. L., . . . Goebel, K. (2011). Battery health management system for electric uavs. In IEEE Aerospace Conference (pp. 1–9).
Saxena, A., Roychoudhury, I., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2012). Requirement flowdown for prognostics health management. In Proceedings of the AIAA Infotech @ Aerospace.
Saxena, A., Roychoudhury, I., Lin, W., & Goebel, K. (2013). Towards requirements in systems engineering for aerospace ivhm design. In Proceedings of the AIAA Infotech @ Aerospace.
Seo, S., Wallat, M., Graepel, T., & Obermayer, K. (2000). Gaussian process regression: Active data selection and test point rejection. In Mustererkennung 2000 (pp. 27– 34). Springer.
Sommerville, I., & Sawyer, P. (1997). Requirements engineering: A good practices guide. John Wiley & Sons.
Tang, L., Saxena, A., Orchard, M. E., Kacprzynski, G. J., Vachtsevanos, G., & Patterson-Hine, A. (2007). Simulation-based design and validation of automated contingency management for propulsion systems. In IEEE Aerospace Conference (pp. 1–11).
Wallace, D. R., & Fujii, R. U. (1989). Software Verification and validation: an overview. Software, IEEE, 6(3), 10– 17.
Yegnanarayana, B. (2004). Artificial neural networks. PHI Learning Pvt. Ltd.
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