A novel PF-LSSVR-based framework for failure prognosis of nonlinear systems with time-varying parameters

Chinese Journal of Aeronautics

Volumn 25, Issue 5, 2012, Pages 715-724

  Chen, Xiongzi ^a   Yu, Jinsong ^a   Tang, Diyin ^a   Wang, Yingxun ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

failure prognosis; least squares support vector regression; nonlinear systems; particle filtering; prognostics and health management; remaining useful life; time varying parameter

Indexed keywords

FAILURE PROGNOSIS; LEAST SQUARES SUPPORT VECTOR REGRESSION; PARTICLE FILTERING; PROGNOSTICS AND HEALTH MANAGEMENTS; REMAINING USEFUL LIVES; TIME VARYING PARAMETER;

ITERATIVE METHODS; MONTE CARLO METHODS; NONLINEAR SYSTEMS; PROBABILITY DISTRIBUTIONS; SIGNAL FILTERING AND PREDICTION; TIME VARYING CONTROL SYSTEMS; WATER TANKS;

FORECASTING;

EID: 84868137682     PISSN: 10009361     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1000-9361(11)60438-X     Document Type: Article

References (19)

1. MG Pecht, R Jaai A prognostics and health management roadmap for information and electronics-rich system Microelectronics Reliability 50 3 2010 317 323
2. JO Berger Statistical decision theory and Bayesian analysis 2nd ed. 1993 Springer Verlag New York
3. MS Arulampalam, S Maskell, N Gordon et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Transactions on Processing 50 2 2002 174 188 (Pubitemid 34291500)
4. Orchard M, Wu B Q, Vachtsevanos G. A particle filtering framework for failure prognosis. Proceedings of WTC2005 World Tribology Congress III, 2005; 883-884. (Pubitemid 43262868)
5. Chen M Z, Zhou D H. Particle filtering based fault prediction of nonlinear systems. IFAC Symposium Proceedings of Safe Process, 2003.
6. L Zhang, XS Li, JS Yu et al. A fault prognostic algorithm based on Gaussian mixture model particle filter Acta Aeronautica et Astronautica Sinica 30 2 2009 319 324 [in Chinese].
7. E Zio, G Peloni Particle filtering prognostic estimation of the remaining useful life of nonlinear components Reliability Engineering and System Safety 96 3 2011 403 409
8. B Saha, K Goebel, S Poll et al. Prognostics methods for battery health monitoring using a Bayesian framework IEEE Transactions on Instrument and Measurement 58 2 2009 291 296
9. Zhang B, Sconyers C, Patrick R, et al. A multi-fault modeling approach for fault diagnosis and failure prognosis of engineering systems. Annual Conference of Prognostics and Health Management Society, 2009; 1-10.
10. CC Chen, B Zhang, G Vachtsevanos et al. Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering IEEE Transactions on Industrial Electronics 58 9 2011 4353 4364
11. JAK Suykens, T van Gestel, J De Brabanter et al. Least squares support vector machines 2002 World Scientific Publishing Co. Pte. Ltd. Singapore
12. BH Xu, HR Li, HF Bai Application of LS-SVM in prediction condition monitoring data Science Technology and Engineering 7 15 2007 3924 3926 [in Chinese].
13. LC Dai, LL Wu, HN Zhao et al. Error prediction based on least squares support vector machines Journal of Central South University: Science Technology 40 Sup.1 2009 253 257 [in Chinese].
14. ZG Xu, YD Ji, DH Zhou Real-time reliability prediction for a dynamic system based on the hidden degradation process identification IEEE Transactions on Reliability 57 2 2008 230 242
15. EA Wan, R van der Merwe, AT Nelson Dual estimation and the unscented transformation. Advances in Neural Information Processing Systems 12 2000 MIT Press Cambridge 666 672
16. L Zhang, XS Li, JS Yu et al. A fault prognostic algorithm based on hybrid system particle filter and dual estimation Acta Aeronautica et Astronautica Sinica 30 7 2009 1 7 [in Chinese].
17. Z Chen Bayesian filtering: from Kalman filters to particle filters, and beyond 2003 McMaster University Hamilton
18. JF Zhang, SS Hu Nonlinear time series fault prediction based on clustering and support vector machines Control Theory and Applications 24 1 2007 64 68 [in Chinese].
19. Saxena A, Celaya J, Saha B, et al. On applying the prognostic performance metrics. Annual Conference of Prognostics and Health Management Society, 2009.