A co-training-based approach for prediction of remaining useful life utilizing both failure and suspension data

Mechanical Systems and Signal Processing

Volumn 62, Issue , 2015, Pages 75-90

  Hu, Chao ^a   Youn, Byeng D ^b   Kim, Taejin ^b   Wang, Pingfeng ^c  

^a UNIVERSITY OF MARYLAND   (United States)

^b Seoul National University   (South Korea)

^c Wichita State University   (United States)

Author keywords

Co training; Data driven prognostics; RUL prediction; Semi supervised learning; Suspension data

Indexed keywords

CONDITION MONITORING; FORECASTING; SEMI-SUPERVISED LEARNING;

CO-TRAINING; CONDITION-MONITORING DATA; DATA-DRIVEN ALGORITHM; DATA-DRIVEN PROGNOSTICS; REMAINING USEFUL LIFE PREDICTIONS; REMAINING USEFUL LIVES; RUL PREDICTIONS; SUSPENSION DATUM;

SUSPENSIONS (COMPONENTS);

EID: 84928617727     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2015.03.004     Document Type: Article

References (45)

1. R. Dekker Applications of maintenance optimization models: a review and analysis Reliab. Eng. Syst. Saf. 51 3 1996 229 240
2. M. Marseguerra, E. Zio, and L. Podofillini Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation Reliab. Eng. Syst. Saf. 77 2 2002 151 165
3. E. Zio Review reliability engineering: Old problems and new challenges Reliab. Eng. Syst. Saf. 94 2 2009 125 141
4. C. Hu, P. Wang, B.D. Youn, and W.R. Lee Copula-Based Statistical Health Grade System against Mechanical Faults of Power Transformers IEEE Transactions on Power Delivery 27 4 2012 1809 1819
5. B.D. Youn, C. Hu, and P. Wang Resilience-Driven System Design of Complex Engineered Systems Journal of Mechanical Design 133 10 2011 101011(15)
6. E. Myotyri, U. Pulkkinen, and K. Simola Application of stochastic filtering for lifetime prediction, Reliab. Eng. Syst. Saf. 91 2 2006 200 208
7. F. Cadini, E. Zio, and D. Avram Model-based Monte Carlo state estimation for condition-based component replacement Reliab. Eng. Syst. Saf. 94 3 2009 752 758
8. J. Luo, K.R. Pattipati, L. Qiao, and S. Chigusa Model-based prognostic techniques applied to a suspension system IEEE Trans. Syst. Man Cybern. A 38 5 2008 1156 1168
9. N. Gebraeel, and J. Pan Prognostic degradation models for computing and updating residual life distributions in a time-varying environment, IEEE Trans. Reliab. 57 4 2008 539 550
10. N. Gebraeel, A. Elwany, and J. Pan Residual life predictions in the absence of prior degradation knowledge IEEE Trans. Reliab. 58 1 2009 106 117
11. X.S. Si, W. Wang, C.H. Hu, M.Y. Chen, and D.H. Zhou A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation, Mech. Syst. Signal Process. 35 1-2 2013 219 237
12. Schwabacher M., 2005, A survey of data-driven prognostics, in: Proceedings of AIAA Infotech@Aerospace Conference, Arlington, VA.
13. X.S. Si, W. Wang, C.H. Hu, and D.H. Zhou Remaining useful life estimation-A review on the statistical data driven approaches Eur. J. Oper. Res. 213 1 2011 1 14
14. Wang T., Yu J., Siegel D., and Lee J., 2008, A similarity-based prognostics approach for remaining useful life estimation of engineered systems, in: Proceedings of the International Conference on Prognostics and Health Management, Denver, CO, Oct 6-9.
15. P. Wang, B.D. Youn, and C. Hu A generic probabilistic framework for structural health prognostic and uncertainty management Mech.l Syst. Signal Process. 28 2012 622 637
16. C. Hu, B.D. Youn, and P. Wang Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life Reliab. Eng. Syst. Saf. 103 2012 120 135
17. Coble J.B., and Hines J.W., 2008, Prognostic algorithm categorization with PHM challenge application, in: Proceedings of the IEEE International Conference on Prognostics and Health Management, Denver, CO, Oct 6-9.
18. Heimes F.O., 2008, Recurrent neural networks for remaining useful life estimation, in: Proceedings of the IEEE International Conference on Prognostics and Health Management, Denver, CO, Oct 6-9.
19. V.T. Tran, H. Thom Pham, B. Yang, and T. Tien Nguyen Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine Mech. Syst. Signal Process. 32 2012 320 330
20. D.A. Tobon-Mejia, K. Medjaher, and N. Zerhouni CNC machine tools wear diagnostic and prognostic by using dynamic Bayesian networks, Mech. Syst. Signal Process. 28 2012 167 182
21. Kozlowski J.D., Watson M.J., Byington C.S., Garga A.K., and Hay T.A., 2001, Electrochemical cell diagnostics using online impedance measurement, state estimation and data fusion techniques, in: Proceedings of the 36th Intersociety Energy Conversion Engineering Conference, Savannah, Georgia.
22. Goebel K., Eklund N., and Bonanni P., 2006, Fusing competing prediction algorithms for prognostics, in: Proceedings of 2006 IEEE Aerospace Conference, New York.
23. B. Saha, K. Goebel, S. Poll, and J. Christophersen Prognostics methods for battery health monitoring using a Bayesian framework IEEE Trans. Instrum. Meas. 58 2 2009 291 296
24. A. Heng, A.C.C. Tan, J. Mathewa, N. Montgomery, D. Banjevic, and A.K.S. Jardine Intelligent condition-based prediction of machinery reliability Mech. Syst. Signal Process. 23 5 2009 1600 1614
25. W. Caesarendra, A. Widodo, and B.-S. Yang Application of relevance vector machine and logistic regression for machine degradation assessment Mech. Syst. Signal Process. 24 4 2010 1161 1171
26. A. Widodo, and B.-S. Yang Application of relevance vector machine and survival probability to machine degradation assessment Expert Syst. Appl. 38 3 2011 2592 2599
27. Z. Tian, L. Wong, and N. Safaei A neural network approach for remaining useful life prediction utilizing both failure and suspension histories, Mech. Syst. Signal Process. 24 5 2010 1542 1555
28. Z.-H. Zhou, and M. Li Semisupervised regression with cotraining-style algorithms IEEE Trans. Knowl. Data Eng. 19 11 2007 1479 1493
29. Abdel Hady M.F., Schwenker F., and Palm G., Semi-supervised learning for regression with co-training by committee in: C. Alippi et al., (Eds.), Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN 2009), LNCS 5768, Springer-Verlag, 2009, pp. 121-130.
30. Z.-H. Zhou, and M. Li Semi-supervised learning by disagreement, Knowl. Inf. Syst. 24 3 2010 415 439
31. Byington C.S., Watson M., and Edwards D., 2004, Data-driven neural network methodology to remaining life predictions for aircraft actuator components, in: Proceedings of IEEE Aerospace Conference, March 6-13, vol. 6, pp. 3581-3589, doi:10.1109/AERO.2004.1368175.
32. Byington C.S., Watson M., and Edwards D., Dynamic signal analysis and neural network modeling for life prediction of flight control actuators,in: Proceedings of the American Helicopter Society 60th Annual Forum, Alexandria, VA: AHS, 2004.
33. Liu J., Saxena A., Goebel K., Saha B., and Wang W., An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries, in: Proceedings of Annual Conference of the PHM Society, October 10-16, Portland, Oregon, 2010.
34. S.J. Wu, N. Gebraeel, M.A. Lawley, and Y. Yih A neural network integrated decision support system for condition-based optimal predictive maintenance policy, IEEE Trans. Syst. Man Cybern A - Syst. Hum. 37 2 2007 226 236
35. M. Hagan, H. Demuth, and M. Beale. Neural Network Design 1996 PWS Publishing Boston, MA
36. J. Park, and I.W. Sandberg Approximation and radial-basis function networks Neural Comput. 5 1993 305 316
37. F. Schwenker, H. Kestler, and G. Palm Three learning phases for radial basis function networks, Neural Netw. 14 4-5 2001 439 458
38. P.D. McFadden, and J.D. Smith Model for the vibration produced by a single point defect in a rolling element bearing J. Sound Vib. 96 1 1984 69 82
39. Y.F. Wang, and P.J. Kootsookos Modeling of low shaft speed bearing faults for condition monitoring Mech. Syst. Signal Process. 12 3 1998 415 426
40. Saxena A., and Goebel K., Damage propagation modeling for aircraft engine run-to-failure simulation,in: Proceedings of the IEEE International Conference on Prognostics and Health Management, Denver, CO, Oct 6-9, 2008.
41. J.F. Bercher, and C. Vignat Estimating the entropy of a signal with applications IEEE Trans. Signal Process. 48 6 2000 1687 1694
42. Y. Shao, and K. Nezu Prognosis of remaining bearing life using neural networks Proc. Inst. Mech. Eng. I, J. Syst. Control Eng. 214 3 2000 217 230
43. Tian X., Cooling fan reliability, failure criteria, accelerated life testing, modeling, and quantification, in: Proceedings of the IEEE Annual Reliability and Maintainability Symposium, Newport Beach, CA, Jan 23-26, 2006.
44. R. Burger Cooling Tower Technology - Maintenance, Updating And Rebuilding 1995 Fairmont Press Lilburn, GA
45. K. Nigam, A.K. McCallum, S. Thrun, and T. Mitchell Text classification from labeled and unlabeled documents using EM Mach. Learn. 39 2-3 2000 103 134