Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.
How to Cite
battery health algorithms, algorithms, GPU
Transactions on Signal Processing, 50(2), 174–188.
Daigle, M., Bregon, A., & Roychoudhury, I. (2012, September). A distributed approach to system-level prognostics. In Annual conference of the prognostics and health management society 2012 (p. 71-82).
Daigle, M., Bregon, A., & Roychoudhury, I. (2014, June). Distributed prognostics based on structural model decomposition. IEEE Transactions on Reliability, 63(2), 495-510.
Daigle, M., & Goebel, K. (2013, May). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 535-546.
Daigle, M., & Kulkarni, C. (2013, October). Electrochemistry-based battery modeling for prognostics. In Annual conference of the prognostics and
health management society 2013 (p. 249-261).
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10, 197–208.
Julier, S. J., & Uhlmann, J. K. (2004, March). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.
Luebke, D. (2008). Cuda: Scalable parallel programming for high-performance scientific computing. In Biomedical imaging: From nano to macro, 2008. isbi 2008. 5th ieee international symposium on (pp. 836–838).
Michalakes, J., & Vachharajani, M. (2008). Gpu acceleration of numerical weather prediction. Parallel Processing Letters, 18(04), 531–548.
Orchard, M., & Vachtsevanos, G. (2009, June). A particle filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31(3-4), 221-246.
Owens, J. D., Luebke, D., Govindaraju, N., Harris, M., Kr¨uger, J., Lefohn, A. E., & Purcell, T. J. (2007). A survey of general-purpose computation on graphics hardware. In Computer graphics forum (Vol. 26, pp. 80–113).
Saha, B., & Goebel, K. (2009, September). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the annual conference of the prognostics and health management society 2009.
Sankararaman, S., Daigle, M., & Goebel, K. (2014, June). Uncertainty quantification in remaining useful life prediction using first-order reliability methods. IEEE Transactions on Reliability, 63(2), 603-619.
Valcarce, A., De La Roche, G., & Zhang, J. (2008). A gpu approach to fdtd for radio coverage prediction. In Communication systems, 2008. iccs 2008. 11th ieee singapore international conference on (pp. 1585–1590).
Wojtkiewicz, S. F., et al. (2011). Use of gpu computing for uncertainty quantification in computational mechanics: A case study. Scientific Programming, 19(4), 199–212.
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