[1] Goebel K, Saha B, Saxena A, et al. Prognostics in battery health management. IEEE Instrumentation and Measurement Magazine, 2008, 11(4): 33-40.
[2] Zeng S K, Pecht M G, Wu J. Status and perspective of prognostics and health management technologies. Acta Aeronautica et Astronautica Sinica, 2005, 26(5): 626-632. (in Chinese) 曾声奎, Pecht M G, 吴际. 故障预测与健康管理(PHM) 技术的现状与发展. 航空学报, 2005, 26(5): 626-632.
[3] Zhang J L, Lee J. A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 2011, 196(15): 6007-6014.
[4] Sun B, Kang R, Zhang S N. An approach to diagnostics and prognostics based on evolutionary feature parameters. Acta Aeronautica et Astronautica Sinica, 2008, 29(2): 393-398. (in Chinese) 孙博, 康锐, 张叔农. 基于特征参数趋势进化的故障诊断和预测方法. 航空学报, 2008, 29(2): 393-398.
[5] Saha B, Goebel K, Poll S, et al. Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 2009, 58(2): 291-296.
[6] He W, Williard N, Osterman M, et al. Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 2011, 196(23): 10314-10321.
[7] Liu J, Saxena A, Goebel K, et al. An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. Proceedings of Annual Conference of Prognostics and Health Management Society, 2010: 1-9.
[8] Liu D T, Luo Y, Peng Y, et al. Lithium-ion battery remaining useful life estimation based on nonlinear AR model combined with degradation feature. Proceedings of Annual Conference of Prognostics and Health Management Society, 2012: 1-7.
[9] Liu D T, Pang J Y, Zhou J B, et al. Data-driven prognostics for lithium-ion battery based on Gaussian process regression. Proceedings of Prognostics and System Health Management Conference, 2012: 1-5.
[10] Engel S J, Gilmartin B J, Bongort K, et al. Prognostics, the real issues involved with predicting life remaining. IEEE Aerospace Conference Proceedings, 2000, 6: 457-469.
[11] Chen X Z, Yu J S, Tang D Y, et al. A novel PF-LSSVR-based framework for failure prognosis of nonlinear systems with time-varying parameters. Chinese Journal of Aeronautics, 2012, 25(5): 715-724.
[12] Suykens J A K, van Gestel T, De Brabanter J, et al. Least squares support vector machines. Singapore: Publishing Co. Pte. Ltd., 2002.
[13] van Gestel T, Suykens J A K, Bart D M, et al. Automatic relevance determination for least squares support vector machine regression. Proceedings of the International Joint Conference on Neural Networks, 2001: 2416-2421.
[14] van Gestel T, Suykens J A K, Baestaens D E, et al. Financial time series prediction using least squares support vector machines with the evidence framework. IEEE Transactions on Neural Network, 2001, 12(4): 809-821.
[15] Takens F. Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, 1981: 366-381.
[16] Cao L Y. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 1997, 110(1): 43-50.
[17] Fishman G S. Monte Carlo concepts, algorithms, and applications. New York: Springer, 1996.
[18] Saha B, Goebel K. Battery data set. NASA Ames Prognostics Data Repository. http://ti.arc.nasa.gov/project/prognostic-data-repository, 2007.
[19] Saxena A, Celaya J, Saha B, et al. On applying the prognostic performance metrics. Annual Conference of Prognostics and Health Management Society, 2009: 1-16. |