Fault prediction model based on evidential reasoning approach

Science in China, Series F: Information Sciences

Volumn 53, Issue 10, 2010, Pages 2032-2046

  Si, Xiao Sheng ^a,b   Hu, Chang Hua ^a   Zhou, Zhi Jie ^a,b  

^a XI'AN RESEARCH INSTITUTE OF HIGH TECHNOLOGY   (China)

^b TSINGHUA UNIVERSITY   (China)

Author keywords

belief distribution; evidential reasoning; fault prediction; nonlinear optimization

Indexed keywords

EID: 77956420798     PISSN: 10092757     EISSN: 18622836     Source Type: Journal    
DOI: 10.1007/s11432-010-4073-y     Document Type: Article

References (50)

1. Chen M Z, Zhou D H. Fault prediction techniques for dynamic systems. Control Theory Appl, 2003, 20: 819-824.
2. Zhou Z J, Hu C H, Zhou D H. Fault prediction techniques for dynamic systems based on nonanalytical model. Inf Control, 2006, 35: 603-608.
3. Zhou D H, Hu Y Y. Fault diagnosis techniques for dynamic systems. Acta Automat Sin, 2009, 35: 748-758.
4. Lu K S, Saeks R. Failure prediction for an on-line maintenance system in a poisson shock environment. IEEE Trans Syst Man Cybern, 1979, 9: 356-362.
5. Zhang Z D, Hu S S. A new method for fault prediction of model-unknown nonlinear system. J Franklin Inst, 2008, 345: 136-153.
6. Yang S K. An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor. Reliab Eng Syst Safe, 2002, 75: 103-111.
7. Zhou D H, Frank P M. Strong tracking filtering of nonlinear time-varying stochastic systems with colored noise with application to parameter estimation and empirical robustness analysis. Int J Control, 1996, 65: 295-370.
8. Wang D, Zhou D H, Jin Y H. A strong tracking predictor for nonlinear processes with input time delay. Comput Chem Eng, 2004, 28: 2523-2540.
9. Zhang L, Li X S, Yu J S, et al. A fault prognostic algorithm based on hybrid system particle filter and dual estimation. Acta Aeronaut Et Astronaut Sin, 2009, 30: 1277-1283.
10. Zhang L, Li X S, Yu J S, et al. A fault prognostic algorithm based on Gaussian mixture model particle filter. Acta Aeronaut Et Astronaut Sin, 2009, 30: 319-324.
11. Hu C H, Zhang Q, Qiao Y K. A strong tracking particle filter with application to fault prediction. Acta Automat Sin, 2008, 34: 1522-1528.
12. Chen M Z, Zhou D H, Liu G P. A new particle predictor for fault prediction of nonlinear time-varying systems. Dev Chem Eng Miner Process, 2005, 13: 379-388.
13. Box G E, Jenkins G M. Time Series Analysis Forecasting and Control. San Francisco: Holden-Day, 1970.
14. Ho S L, Xie M. The use of ARIMA models for reliability forecasting and analysis. Comput Ind Eng, 1998, 35: 213-216.
15. Chen H T, Huang W H, Jiang X W. Grey model based fault forecasting technique and its application in propulsion system of space. J Propul Tech, 1998, 19: 74-77.
16. Zhang L B, Wang Z H, Zhao S X. Short-term fault prediction of mechanical rotating parts on the basis of fuzzy-grey optimizing method. Mech Syst Signal Process, 2007, 21: 856-865.
17. Lapedes A, Farber R. Nonlinear signal processing using neural networks prediction and system modeling. USA: Los Alamos National Laboratory, 1987.
18. Zhang G Z, Huang D S, Quan Z H. Combining a binary input encoding scheme with RBFNN for globulin protein inter-residue contact map prediction. Pattern Recogn Lett, 2005, 26: 1543-1553.
19. Liang Y H. Evolutionary neural network modeling for forecasting the field failure data of repairable systems. Exp Syst Appl, 2007, 33: 1090-1096.
20. Hu S S, Zhang Z D. Fault prediction for nonlinear time series based on neural network. Acta Automat Sin, 2007, 33: 744-748.
21. Li R Y, Kang R. Failure rate forecasting method based on neural networks. Acta Aeronaut Et Astronaut Sin, 2008, 29: 357-363.
22. Wang Y M, Elhag M S T. A comparison of neural network, evidential reasoning and multiple regression analysis in modeling bridge risks. Exp Syst Appl, 2007, 32: 336-348.
23. Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, 1995.
24. Pai P F, Hong W C. Software reliability forecasting by support vector machines with simulated annealing algorithms. J Syst Softw, 2006, 79: 747-755.
25. Pai P F. Systems reliability forecasting based on support vector machines with genetic algorithms. Math Comput Model, 2006, 43: 262-247.
26. Chen K Y. Forecasting systems reliability based on support vector regression with genetic algorithms. Reliab Eng Syst Safe, 2007, 92: 423-432.
27. Li H R, Xu B H. Fault prognosis of hydraulic pump in the missile launcher. Acta Armamentarii, 2009, 30: 900-906.
28. Wang Y, Guo W. Local prediction of the chaotic fh-code based on LS-SVM. J Syst Eng Electron, 2008, 19: 65-70.
29. Dimitras A I, Slowinski R, Susmaga R, et al. Business failure prediction using rough sets. Eur J Oper Res, 1999, 114: 263-280.
30. Dong M. A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (ARHSMM). Sci China Ser F-Inf Sci, 2008, 51: 1291-1304.
31. Angeli C, Chatzinikolaou A. Fault prediction and compensation functions in a diagnostic knowledge-based system for hydraulic systems. J Intell Robot Syst-Theory Appl, 1999, 26: 153-165.
32. Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomput, 2003, 50: 159-175.
33. Yang J B, Wang Y M, Xu D L, et al. The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties. Euro J Oper Res, 2006, 171: 309-343.
34. Yang J B, Liu J, Wang J, et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Trans Syst Man Cybern A, 2006, 30: 266-285.
35. Yang J B, Liu J, Xu D L, et al. Optimization models for training belief-rule-based systems. IEEE Trans Syst Man Cybern A, 2007, 37: 569-585.
36. Yang J B, Singh M G. An evidential reasoning approach for multiple-attribute decision making with uncertainty. IEEE Trans Syst Man Cybern, 1994, 24: 1-18.
37. Dempster A P. Upper and lower probabilities induced by a multi-valued mapping. Ann Math Stat, 1967, 38: 325-339.
38. Shafer G. A Mathematical Theory of Evidence. Princeton, New Jersey: Princeton University Press, 1976.
39. Yang J B. Rule and utility based evidential reasoning approach for multi-attribute decision analysis under uncertainties. Euro J Oper Res, 2001, 131: 31-61.
40. Yang J B, Xu D L. On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans Syst Man Cybern A, 2002, 32: 289-304.
41. Yang J B, Xu D L. Nonlinear information aggregation via evidential reasoning in multiattribute decision analysis under uncertainty. IEEE Trans Syst Man Cybern A, 2002, 32: 376-393.
42. Wang Y M, Yang J B, Xu D L. Environmental impact assessment using the evidential reasoning approach. Euro J Oper Res, 2006, 174: 1885-1913.
43. Wang Y M, Elhag M S T. Evidential reasoning approach for bridge condition assessment. Exp Syst Appl, 2008, 34: 689-699.
44. Xu D L, Liu J, Yang J B, et al. Inference and learning methodology of belief-rule-based expert system for pipeline leak detection. Exp Syst Appl, 2007, 32: 103-113.
45. Herrera L J, Pomares H, Rojas I, et al. Recursive prediction for long term time series forecasting using advanced models. Neurocomput, 2007, 70: 2870-2880.
46. Xu K, Xie M, Tang L C, et al. Application of neural networks in forecasting engine systems reliability. Appl Soft Comput J, 2003, 2: 255-268.
47. Hu C H, Si X S, Zhou Z J, et al. An improved D-S algorithm under the new measure criteria of evidence conflict. Acta Electron Sin, 2009, 37: 1578-1583.
48. Zhang S G, Li W H, Ding K. A novel approach to evidence combination based on the evidence credibility. Control Theory Appl, 2009, 26: 812-814.
49. Jousselme A L, Grenier D, Bossé E. A new distance between two bodies of evidence. Inform Fusion, 2001, 2: 91-101.
50. Hu C H, Si X S, Yang J B. Systems reliability forecasting based on evidential reasoning algorithm with nonlinear optimization. Exp Syst Appl, 2010, 37: 2550-2562.