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Fault prediction model based on evidential reasoning approach

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Abstract

In order to deal with fault prediction problems that involve both quantitative and qualitative information for nonlinear complex system, a new fault prediction model is established based on the evidential reasoning (ER) approach, and an optimal learning algorithm for training ER-based prediction model is presented based on the mean square error (MSE) criterion. This prediction model inherits the advantages of ER approach, which can deal with precise data, incomplete data and fuzzy data with nonlinear characteristic. In this model, the input signals transformed using rule based information transformation technique, are aggregated by analytical ER approach, and then the outputs of prediction model are constructed according to the types of system outputs. In addition, two fault decision criteria are defined to conduct fault identification. To overcome the difficulty in determining model parameters accurately and subjectively, a nonlinear optimization model is constructed and the optimal parameters are obtained. Two experimental studies are conducted to evaluate the performance of the proposed model. The results show that the established prediction model and the presented parameters optimization methods can deal with fault prediction problem effectively.

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References

  1. Chen M Z, Zhou D H. Fault prediction techniques for dynamic systems. Control Theory Appl, 2003, 20: 819–824

    Google Scholar 

  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

    Google Scholar 

  3. Zhou D H, Hu Y Y. Fault diagnosis techniques for dynamic systems. Acta Automat Sin, 2009, 35: 748–758

    Article  Google Scholar 

  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

    Article  MATH  MathSciNet  Google Scholar 

  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

    Article  MATH  MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MATH  MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  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

    MathSciNet  Google Scholar 

  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

    MathSciNet  Google Scholar 

  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

    MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  13. Box G E, Jenkins G M. Time Series Analysis Forecasting and Control. San Francisco: Holden-Day, 1970

    MATH  Google Scholar 

  14. Ho S L, Xie M. The use of ARIMA models for reliability forecasting and analysis. Comput Ind Eng, 1998, 35: 213–216

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  17. Lapedes A, Farber R. Nonlinear signal processing using neural networks prediction and system modeling. USA: Los Alamos National Laboratory, 1987

    Google Scholar 

  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

    Article  Google Scholar 

  19. Liang Y H. Evolutionary neural network modeling for forecasting the field failure data of repairable systems. Exp Syst Appl, 2007, 33: 1090–1096

    Article  Google Scholar 

  20. Hu S S, Zhang Z D. Fault prediction for nonlinear time series based on neural network. Acta Automat Sin, 2007, 33: 744–748

    MATH  Google Scholar 

  21. Li R Y, Kang R. Failure rate forecasting method based on neural networks. Acta Aeronaut Et Astronaut Sin, 2008, 29: 357–363

    Google Scholar 

  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

    Article  Google Scholar 

  23. Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer, 1995

    MATH  Google Scholar 

  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

    Article  Google Scholar 

  25. Pai P F. Systems reliability forecasting based on support vector machines with genetic algorithms. Math Comput Model, 2006, 43: 262–247

    Article  MATH  MathSciNet  Google Scholar 

  26. Chen K Y. Forecasting systems reliability based on support vector regression with genetic algorithms. Reliab Eng Syst Safe, 2007, 92: 423–432

    Article  Google Scholar 

  27. Li H R, Xu B H. Fault prognosis of hydraulic pump in the missile launcher. Acta Armamentarii, 2009, 30: 900–906

    Google Scholar 

  28. Wang Y, Guo W. Local prediction of the chaotic fh-code based on LS-SVM. J Syst Eng Electron, 2008, 19: 65–70

    MathSciNet  Google Scholar 

  29. Dimitras A I, Slowinski R, Susmaga R, et al. Business failure prediction using rough sets. Eur J Oper Res, 1999, 114: 263–280

    Article  MATH  Google Scholar 

  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

    Article  MATH  Google Scholar 

  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

    Article  Google Scholar 

  32. Zhang G P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomput, 2003, 50: 159–175

    Article  MATH  Google Scholar 

  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

    Article  MATH  MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  37. Dempster A P. Upper and lower probabilities induced by a multi-valued mapping. Ann Math Stat, 1967, 38: 325–339

    Article  MATH  MathSciNet  Google Scholar 

  38. Shafer G. A Mathematical Theory of Evidence. Princeton, New Jersey: Princeton University Press, 1976

    MATH  Google Scholar 

  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

    Article  MATH  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  MATH  MathSciNet  Google Scholar 

  43. Wang Y M, Elhag M S T. Evidential reasoning approach for bridge condition assessment. Exp Syst Appl, 2008, 34: 689–699

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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.

    Google Scholar 

  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

    Google Scholar 

  49. Jousselme A L, Grenier D, Bossé E. A new distance between two bodies of evidence. Inform Fusion, 2001, 2: 91–101

    Article  Google Scholar 

  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

    Article  Google Scholar 

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Authors and Affiliations

  1. Unit 302, Xi’an Institute of Hi-Tech, Shaanxi, Xi’an, 710025, China

    XiaoSheng Si, ChangHua Hu & ZhiJie Zhou

  2. Department of Automation, Tsinghua University, Beijing, 100084, China

    XiaoSheng Si & ZhiJie Zhou

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  1. XiaoSheng Si
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  2. ChangHua Hu
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  3. ZhiJie Zhou
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Correspondence to XiaoSheng Si.

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Si, X., Hu, C. & Zhou, Z. Fault prediction model based on evidential reasoning approach. Sci. China Inf. Sci. 53, 2032–2046 (2010). https://doi.org/10.1007/s11432-010-4073-y

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