NARX Time Series Model for Remaining Useful Life Estimation of Gas Turbine Engines
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Prognostics is a promising approach used in condition based maintenance due to its ability to forecast complex systems' remaining useful life. In gas turbine maintenance applications,
data-driven prognostic methods develop an understanding of system degradation by using regularly stored condition monitoring data, and then can automatically monitor and evaluate the future health index of the system. This paper presents such a technique for fault prognosis for turbofan engines. A prognostic model based on a nonlinear autoregressive neural network design with exogenous input is designed to determine how the future values of wear can be predicted. The research applies the life prediction as a type of dynamic filtering, in which training time series are used to predict the future values of test series. The results demonstrate the relationship between the historical performance deterioration of an engine's prior operating period with the current life prediction.
How to Cite
##plugins.themes.bootstrap3.article.details##
data driven prognostics, Neural Networks, NARX, multi step prediction
Barad, S. G., Ramaiah, P., Giridhar, R., & Krishnaiah, G. (2012). Neural network approach for a combined performance and mechanical health monitoring of a gas turbine engine. Mechanical Systems and Signal Processing, 27, 729–742.
Beale, M., & Demuth, H. (1998). Neural network toolbox. For Use with MATLAB, User’s Guide, The MathWorks, Natick, 1–6.
Byington, C. S., Watson, M., & Edwards, D. (2004). Data-driven neural network methodology to remaining life predictions for aircraft actuator components. In Aerospace conference, 2004. proceedings. 2004 ieee (Vol. 6, pp. 3581–3589).
Galar, D., Kumar, U., Lee, J., & Zhao, W. (2012). Remaining useful life estimation using time trajectory tracking and support vector machines. In Journal of physics: Conference series (Vol. 364, p. 012063).
Haykin, S., & Li, X. B. (1995). Detection of signals in chaos. Proceedings of the IEEE, 83(1), 95–122.
Haykin, S., & Principe, J. (1998). Making sense of a complex world [chaotic events modeling]. Signal Processing Magazine, IEEE, 15(3), 66–81.
Heath, G. (2015, March). Narnet tutorial on multistep ahead predictions. [Online forum comment] https://uk.mathworks.com/matlabcentral /newsreader/view thread/338508.
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739.
Hogg, R. V., & Craig, A. T. (1995). Introduction to mathematical statistics: 5th ed. Macmillan.
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483–1510.
Krenker, A., Kos, A., & Bešter, J. (2011). Introduction to the artificial neural networks. INTECH Open Access Publisher.
Menezes, J. M. P., & Barreto, G. A. (2008). Long-term time series prediction with the narx network: an empirical evaluation. Neurocomputing, 71(16), 3335–3343.
Murata, N., Yoshizawa, S., & Amari, S.-i. (1994). Network information criterion-determining the number of hidden units for an artificial neural network model. Neural Networks, IEEE Transactions on, 5(6), 865–872.
Pecht, M. (2008). Prognostics and health management of electronics. Wiley Online Library.
Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: a review. The International Journal of Advanced Manufacturing Technology, 50(1-4), 297–313.
Sarle, W. S. (1994). Neural networks and statistical models.
Saxena, A., & Goebel, K. (2008). Turbofan engine degradation simulation data set. NASA Ames Prognostics Data Repository.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine runto-failure simulation. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–9).
Schwabacher, M., & Goebel, K. (2007). A survey of artificial intelligence for prognostics. In Aaai fall symposium (pp. 107–114).
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011). Remaining useful life estimation–a review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1–14.
Siegelmann, H. T., Horne, B. G., & Giles, C. L. (1997). Computational capabilities of recurrent narx neural networks. Systems, Man, and Cybernetics, Part B Cybernetics, IEEE Transactions on, 27(2), 208 215.
Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., & Lendasse, A. (2007). Methodology for long-term prediction of time series. Neurocomputing, 70(16), 2861–2869.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.