Open Access
Mechanics & Industry
Volume 20, Number 1, 2019
Article Number 106
Number of page(s) 14
Published online 08 April 2019
  1. X. Zhang, X. Chen, B. Li, Life prediction of machinery major equipment: a review, J. Mech. Eng. 47 (2011) 100–116 [CrossRef] [Google Scholar]
  2. N.M. Vichare, M.G. Pecht, Prognostics and health management of electronics, IEEE Trans. Compon. Pack. Technol. 29 (2006) 222–229 [CrossRef] [Google Scholar]
  3. Y. Lei, Z. He, Z. Yanyang, Fault diagnosis based on the new model of hybrid intelligence, Mech. Eng. 44 (2008) 112–117 [CrossRef] [Google Scholar]
  4. N. Vapnik Vladimir, Statistical learning theory, Wiley, NY, 1998, pp. 760–768 [Google Scholar]
  5. M. Sunghwan, L. Jumin, H. Ingoo, Hybrid genetic algorithms and support vector machines for bankruptcy prediction, Exp. Syst. Appl. 31 (2006) 652–660 [CrossRef] [Google Scholar]
  6. M. Nizam, M. Azah, H. Aini, Dynamic voltage collapse prediction in power systems using support vector regression, Exp. Syst. Appl. 37 (2020) 3730–3736 [CrossRef] [Google Scholar]
  7. H. Dong, X. Jin, Y. Lou, Lithiumion battery state of health monitoring and remaining useful life prediction based on support vector Regression-Particle filter, J. Power Source 2014 (2014) 114–123 [CrossRef] [Google Scholar]
  8. J. Llinas, D.L. Hall, An introduction to multi-sensor data fusion, IEEE Inter. Sym. Circ. Syst. 6 (1998) 537–540 [Google Scholar]
  9. S. Wu, W. Jiang, Research on data fusion fault diagnosis method based on D-S evidence theory, IEEE Comp. Soc. 1 (2009) 689–692 [Google Scholar]
  10. J. Tian, W. Zhao, R. Du, D-S Evidence Theory and its Data Fusion Application in Intrusion Detection, Springer, Berlin, Heidelberg, CA, 2000, pp. 244–251 [Google Scholar]
  11. H. Sorenson, D. Alspach, Recursive Bayesian estimation using Gaussian sums, J. Auto. 7 (1971) 465–479 [CrossRef] [Google Scholar]
  12. B. Ristic, S. Arulampalam, N. Gordon, Beyond the Kalman filter-particle filters for tracking applications, IEEE. Trans. Aero. Electr. Syst. 19 (2004) 37–38 [Google Scholar]
  13. J. Carpenter, P. Clifford, P. Fearnhead, Improved particle filter for nonlinear problems, IEEE Proc. Radar. Sonar. Navig. 146 (1999) 2–7 [CrossRef] [Google Scholar]
  14. A.F. Seila, Simulation and the Monte Carlo method, Tech. 24 (2012) 167–168 [Google Scholar]
  15. N. Metropolis, S. Ulam, The Monte Carlo method, J. Am. Tatis. Assoc. 60 (1948) 115–129 [Google Scholar]
  16. J. Carpenter, P. Clifford, P. Fearnhead, Improved particle filter for nonlinear problems, IEEE Proc. Radar. Sonar Navig. 146 (1999) 1–7 [Google Scholar]
  17. R. Kalman, A new approach linear frittering prediction problems, Trans. ASME J. Basic Eng. 81 (1960) 35–45 [Google Scholar]
  18. Bishop, Pattern Recognition and Machine Learning, Springer, New York, CA, 2006, pp. 339–344 [Google Scholar]
  19. Z. Yinliang, Z. Changpeng, H. Bo, Z. Qinghua, Runtime support for type-safe and context-based behavior adaptation, in: Presented at Computer and Information Technology (CIT), 2012 IEEE 12th International Conference, 2012 [Google Scholar]
  20. N. Kabaoglu, Target tracking using particle filters with support vector regression, IEEE Trans. Veh. Technol. 58 (2009) 2569–2573 [CrossRef] [Google Scholar]
  21. V. Vapnik, The Nature of Statistical Learning, Springer, Berlin, CA, 1995, pp. 225–259 [Google Scholar]
  22. G. Yao, K. Qingci, A. Yuhua, Method for eliminating data outliers based on wavelet transform, J. Air. Spac. TT&C. Technol. 25 (2006) 64–67 [Google Scholar]
  23. W. Lin, W. Liu, Establishment and application of spring maize yield to evapotranspiration boundary function in the Loess Plateau of China, Agric. Waste Manag. 178 (2016) 345–349 [CrossRef] [Google Scholar]
  24. C. Anagnostopoulos, Quality-optimized predictive analytics, Appl. Intel. 45 (2016) 1–13 [CrossRef] [Google Scholar]
  25. F. Zheng, L.Y. Liu, X.X. Liu, Y. Li, X.G. Shi, G.Y. Zhang, K.W. Huan, Study on outliers influence in NIR quantitative analysis model, Guang Pu Xue Yu Guang Pu Fen Xi 36 (2016) 3523–3529 [PubMed] [Google Scholar]
  26. P. Zhang, J. Chang, B. Qu, Q. Zhao, Denoising and trend terms elimination algorithm of accelerometer signals, Math. Prob. Eng. 2016 (2016) 1–9 [Google Scholar]
  27. E.L. Andreas, G. Treviño, Using wavelets to detect trends, J. Atmos. Ocean Technol. 12 (1997) 554–564 [CrossRef] [Google Scholar]
  28. S. Chen, S.A. Billings, W. Luo, Orthogonal least squares methods and their application to non-linear system identification, Int. J. Control 50 (1989) 1873–1896 [CrossRef] [Google Scholar]
  29. C. Torrence, G.P. Compo, A practical guide to wavelet analysis, Bull. Am. Meteo. Soc. 79 (1998) 61–78 [Google Scholar]
  30. D.E. Newland, Wavelet analysis of vibration: Part 1—Theory, J. Vib. Acoust. 116 (1994) 21–37 [Google Scholar]
  31. I. Daubechies, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Inf. Theory 36 (1990) 961–1005 [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  32. Z.K. Peng, F.L. Chu, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mech. Syst. Sig. Process 18 (2004) 199–221 [CrossRef] [Google Scholar]
  33. D. Hancheng, J. Xiaoning, L. Yangbing, Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter, J. Powder Source 2014 (2014) 114–123 [Google Scholar]
  34. W. Changhong, D. Hancheng, Remaining effective working time prediction method for vehicle lithium ion battery, J. Auto. Eng. 37 (2015) 476–479 [Google Scholar]
  35. Y. Lei, A model-based method for remaining useful life prediction of machinery, IEEE Trans. Relia. 65 (2016) 1–13 [CrossRef] [Google Scholar]
  36. Y. Jie, Z. Xiaodong, Analysis and application of rolling bearing life calculation method, Petro. Mach. 32 (2004) 27–29 [Google Scholar]

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