Estimation of Remaining Useful Life Based on Switching Kalman Filter Neural Network Ensemble
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Abstract
The proposed method is an extension of an existing Kalman Filter (KF) ensemble method. While the original method has shown great promise in the earlier PHM 2008 Data Challenge, the main limitation of the KF ensemble is that it is only applicable to linear models. In prognostics, degradation of mechanical systems is typically non-linear in nature, therefore limiting the applications of KF ensemble in this area. To circumvent this problem, this paper propose to approximate non-linear functions with piecewise linear functions. When estimating the RUL, the Switching Kalman Filter (SKF) is able to choose the most probable degradation mode and thus make better predictions. The implementation of the proposed SKF ensemble method is illustrated by implementing on NASA’s C-MAPSS Dataset as well as the PHM 2008 Data Challenge Dataset. The results show the effectiveness of the SKF in detecting the switching point between various degradation modes as well as the improved accuracy of the SKF ensemble method compared to other available methods in literature.
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Baraldi, P., Mangili, F., & Zio, E. (2012). A kalman filter- based ensemble approach with application to turbine creep prognostics. IEEE TRANSACTIONS ON RELIABILITY, 61.
Borguet, S., & Le ́onard, O. (2009). Coupling principal component analysis and kalman filtering algorithms for online aircraft engine diagnostics. Control Engineering
Practice, 17(4), 494–502.
Heimes, F. (2008). Recurrent neural networks for remaining useful life estimation. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–6).
Krogh, A., & Vedelsby, J. (1995). Neural network ensembles, cross validation, and active learning. Advances in neural information processing systems, 231–238.
Le Son, K., Fouladirad, M., & Barros, A. (2012). Remaining useful life estimation on the non-homogenous gamma with noise deterioration based on gibbs filtering: A case study. In Prognostics and health management (phm), 2012 IEEE conference on (pp. 1–6).
Maclin, R., & Opitz, D. (2011). Popular ensemble methods: An empirical study. arXiv preprint arXiv:1106.0257.
Murphy, K. P. (1998). Switching kalman filters (Tech. Rep.). Citeseer.
Peel, L. (2008). Data driven prognostics using a kalman filter ensemble of neural network models. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–6).
Puskorius, G. V., & Feldkamp, L. A. (1991). Decoupled extended kalman filter training of feedforward layered networks. In Neural networks, 1991., ijcnn-91-seattle international joint conference on (Vol. 1, pp. 771– 777).
Re, M., & Valentini, G. (2011). Ensemble methods: a review. Saxena, A., & Goebel, K. (2008). Phm08 challenge data set, nasa ames prognostics data repository. Moffett Field, CA. Retrieved from[http://ti.arc.nasa.gov/project/prognostic-data-repository]
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run- to-failure simulation. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–9).
Singhal, S., & Wu, L. (1989). Training feed-forward networks with the extended kalman algorithm. In Acoustics, speech, and signal processing, 1989. icassp-89., 1989 international conference on (pp. 1187–1190).
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008). A similarity- based prognostics approach for remaining useful life estimation of engineered systems. In Prognostics and health management, 2008. phm 2008. international conference on (pp. 1–6).
Zhou, Z.-H. (2012). Ensemble methods: foundations and algorithms. CRC Press.
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