Approaches for component degradation modelling in time-varying environments with application to residual life prediction

Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering

Volumn 226, Issue 2, 2012, Pages 117-131

  You, M Y ^a   Meng, G ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

condition based maintenance; degradation modelling; prognostics; residual life distribution; residual life prediction; time varying environments

Indexed keywords

BALL GRID ARRAYS; DEGRADATION; FORECASTING; SOLDERED JOINTS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; TABLE LOOKUP;

CONDITION BASED MAINTENANCE; PROGNOSTICS; RESIDUAL LIFE DISTRIBUTION; RESIDUAL LIFE PREDICTION; TIME-VARYING ENVIRONMENTS;

REAL TIME SYSTEMS;

EID: 84864429170     PISSN: 09544089     EISSN: None     Source Type: Journal    
DOI: 10.1177/0954408911413222     Document Type: Article

References (24)

1. Kacprzynskl G. J., Sarlashkar A., Roemer M. J., Hess A., Hardman W.. Predicting remaining life by fusing the physics of failure modeling with diagnostics. JOM. 2004 ; 56 (3). 29-35
2. Christer A. H., Wang W., Sharp J. M.. A state space condition monitoring model for furnace erosion prediction and replacement. Eur. J. Oper. Res.. 1997 ; 101 (1). 1-14 (Pubitemid 127399910)
3. Pedregal D. J., Carnero M. C.. State space models for condition monitoring: a case study. Reliab. Eng. Syst. Saf.. 2006 ; 91 (2). 171-180 (Pubitemid 41398123)
4. Jardine A. K. S., Lin D., Banjevic D.. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process.. 2006 ; 20: 1483-1519
5. Gebraeel N., Lawley M. A., Li R., Ryan J. K.. Residual-life distributions from component degradation signals: a Bayesian approach. IIE Trans.. 2005 ; 37: 543-557 (Pubitemid 40708863)
6. Huang R., Xi L., Li X., Liu C. R., Qiu H., Lee L.. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mech. Syst. Signal Process.. 2007 ; 21: 193-207 (Pubitemid 44550081)
7. Camci F. Process monitoring, diagnostics and prognostics using support vector machines and hidden Markov models. Detroit, MI: PhD Dissertation, Department of Industrial Engineering, Wayne State University ; 2005 :
8. Kaiser K. A., Gebraeel N.. Predictive maintenance management using sensor-based degradation models. IEEE Trans. Syst. Man Cybern., A, Syst. Humans. 2009 ; 39: 840-849
9. You M. Y., Li L., Meng G., Ni J.. Cost-effective updated sequential predictive maintenance policy for continuously monitored degrading systems. IEEE Trans. Autom. Sci. Eng.. 2010 ; 7 (2). 257-265
10. You M. Y., Liu F., Wang W., Meng G.. Statistically planned and individually improved predictive maintenance management for continuously monitored degrading systems. IEEE Trans. Reliab.. 2010 ; 59 (4). 744-753
11. Heng A., Zhang S., Tan A. C. C., Mathew J.. Rotating machinery prognostics: state of the art, challenges and opportunities. Mech. Syst. Signal Process.. 2009 ; 23: 724-739
12. Ji H. C., Jie M.. Study of a stochastic failure model in a random environment. J. Appl. Probab.. 2007 ; 44 (1). 151-163
13. Gebraeel N., Pan J.. Prognostic degradation models for computing and updating residual life distributions in a time-varying environment. IEEE Trans. Reliab.. 2008 ; 57 (4). 539-550
14. Doksum K. A., Hóyland A.. Models for variable-stress accelerated life testing experiments based on wiener processes and the inverse Gaussian distribution. Technometrics. 1992 ; 34 (1). 74-82
15. Kharoufeh J. P., Cox S. M.. Stochastic models for degradation-based reliability. IIE Trans.. 2005 ; 37 (6). 533-542 (Pubitemid 40708862)
16. Kharoufeh J. P., Solo C. J., Ulukus M. Y.. Semi-Markov models for degradation-based reliability. IIE Trans.. 2010 ; 42 (8). 599-612
17. Gebraeel N.. Sensory-updated residual life distributions for components with exponential degradation patterns. IEEE Trans. Autom. Sci. Eng.. 2006 ; 3 (4). 382-393 (Pubitemid 44574332)
18. Gebraeel N., Elwany A., Pan J.. Residual life predictions in the absence of prior degradation knowledge. IEEE Trans. Reliab.. 2009 ; 58 (1). 106-117
19. Chakraborty S., Gebraeel N., Lawley M., Wan H.. Residual-life estimation for components with non-symmetric priors. IIE Trans.. 2009 ; 41 (4). 372-387
20. Meeker W. Q., Escobar L. A. Statistical methods for reliability data. New York: Wiley ; 1998 :
21. Cox D. R.. Regression models and life-tables (with discussion). J. R. Stat. Soc. B. 1972 ; 34 (2). 187-220
22. Zhao X., Fouladirad M., Bérenguer C., Bordes L.. Condition-based inspection/replacement policies for non-monotone deteriorating systems with environmental covariates. Reliab. Eng. Syst. Saf.. 2010 ; 95 (8). 921-934
23. You M. Y., Liu F., Meng G.. Benefits from condition monitoring techniques: a case study on maintenance scheduling of BGA solder joints.. :
24. Liu F., Meng G., Zhao M., Zhao J. F.. Board level drop test analysis based on modal test and simulation. J. Electron. Packag. 2008 ; 130 (2) ):