An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Oct 10, 2010
Jie Liu Abhinav Saxena Kai Goebel Bhaskar Saha Wilson Wang

Abstract

Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.

How to Cite

Liu, J., Saxena, A. ., Goebel, K. ., Saha, B. ., & Wang, W. . (2010). An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1896
Abstract 1376 | PDF Downloads 493

##plugins.themes.bootstrap3.article.details##

Keywords

data driven prognostics, recurrent neural networks, Remaining useful Life, performance evaluation, battery health management

References
M. Pecht and R. Jaai (2010). A prognostics and health management roadmap for information and electronics-rich systems, Microelectronics Reliability, vol. 50, pp. 317-323, 2010.

D. E. Adams (2002). Nonlinear damage models for diagnosis and prognosis in structural dynamic systems, in Proceedings of SPIE 4733, pp. 180-191, 2002.

A. Atiya, S. El-Shoura, S. Shaheen, M. El-Sherif (1999). A comparison between neural-network forecasting techniques-case study: river flow forecasting, IEEE Transactions on Neural Networks, vol. 10, pp. 402-409, 1999.

D. R. Brillinger (1970). The identification of polynomial systems by means of higher order spectra, Journal of Sound and Vibration, vol. 12, pp. 301-313, 1970.

D. Chelidze and J. P. Cusumano (2004). A dynamical systems approach to failure prognosis, Journal of Vibration and Acoustics, vol. 126, pp. 2-8, 2004.

J. P. Christophersen, I. Bloom, J. P. Thomas, K. L. Gering, G., L. Henriksen, V. S. Battaglia, and D. Howell (2006). Advanced Technology Development Program for Lithium-Ion Batteries: Gen 2 Performance Evaluation Final Report, Information Bridge: DOE Scientific and Technical Information, [Online] Available INL/EXT-05-0091, 3 July 2006.

J. H. Friedman (1991). Multivariate adaptive regression splines, Annals of Statistics, vol. 19, pp. 1-141, 1991.

J. H. Friedman and W. Stuetzle (1981). Projection pursuit regression, Journal of the American Statistical Association, vol. 76, pp. 817-823, 1981.

L. Gao, S. Liu, and R. A. Dougal (2002). Dynamic lithium-ion battery model for system simulation, IEEE Transactions on Components and Packaging Technologies, vol. 25, no. 3, pp. 495-505, 2002.

K. Goebel, B. Saha, A. Saxena, J. R. Celaya, and J. P. Christophersen (2008). Prognostics in battery health management, IEEE Instrumentation & Measurement Magazine, pp. 33-40, August 2008.

K. Goebel, B. Saha, and A. Saxena (2008). A comparison of three data-driven techniques for prognostics, in Proceedings of the 62nd Meeting of the Society for Machinery Failure Prevention Technology (MFPT), pp. 119-131, Virginia Beach,VA, May 2008.

C. Groot and D. Wurtz (1991). Analysis of univariate time series with connectionist nets: A case study of two classical examples, Neurocomputing, vol. 3, pp. 177-192, 1991.

S. Gupta and A. Ray (2007). Real-time fatigue life estimation in mechanical structures, Measurement Science and Technology, vol. 18, pp. 1947-1957, 2007.
J. Jang (1993). ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 23, pp. 665-685, 1993.

J. Jang, C. T. Sun, and E. Mizutani (1997). Neuro- Fuzzy and Soft Computing: A computational approach to learning and machine intelligence, Prentice Hall, pp. 129-193, 1997.

J. Jang, C. Sun, and E. Mizutani (1997). Neuro-fuzzy soft computing, Upper Saddle River, NJ: Prentice- Hall, 1997.

A. Jardine, D. Lin, D. Banjevic (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, vol. 20, pp. 1483-510, 2006.

J. Korbicz (2004). Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer, Berlin, 2004.

J. Liu, W. Wang, and F. Golnaraghi (2009). A multi-
using a Bayesian framework, IEEE Transactions on Instrumentation and Measurement, vol. 58, pp. 291- 296, 2009.

B. Saha and K. Goebel (2009). Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework, in Annual Conference of the PHM Society, San Diego CA, 2009.

B. Saha and K. Goebel (2008). Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques, in Proceedings of IEEE Aerospace Conference, Big Sky MT, 2008.

A. Saxena, J. Celaya, B. Saha, S. Saha, and K. Goebel (2009). Evaluating algorithm performance metrics tailored for prognostics, in Proceedings of IEEE Aerospace Conference, Big Sky MT, 2009.

A. Saxena, J. Celaya, B. Saha, S. Saha, and K. Goebel (2010). Metrics for Offline Evaluation of Prognostics Performance”, International Journal of Prognostics and Health Management, Vol.1(1), pp. 20, 2010.

M. Schwabacher and K. Goebel (2007). A survey of artificial intelligence for prognostics, AAAI Fall Symposium, 2007.

T. Subba Rao (1981). On the theory of bilinear time series models, Journal of the Royal Statistical Society, vol. 43, pp. 244-255, 1981.

H. Tong and K. S. Lim (1980). Threshold autoregression, limited cycles and cyclical data, Journal of the Royal Statistical Society, vol. 42, pp. 245-292, 1980.

P. Tse and D. Atherton (1999). Prediction of machine deterioration using vibration based fault trends and recurrent neural networks, Journal of Vibration and Acoustics, vol. 121, pp. 355-362, 1999.

W. Wang (2007). An adaptive predictor for dynamic system forecasting, Mechanical Systems and Signal Processing, vol. 21, pp. 809-823, 2007.
Section
Technical Research Papers

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 > >>