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Particle filtering-based methods for time to failure estimation with a real-world prognostic application

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Abstract

One of core technologies for prognostics is to predict failures before they occur and estimate time to failure (TTF) by using built-in predictive models. The predictive model could be either physics-based model or machine learning-based model. Machine learning-based predictive modeling is an emerging application of machine learning to machinery maintenance. Accurate TTF estimation could help performing predictive action “just-in-time”. However, the developed predictive models sometimes fail to provide a precise TTF estimate. To address this issue, we propose a Particle Filtering (PF)-based method to estimate TTF. After introducing the PF-based algorithm, we present the implementation along with the experimental results obtained from a case study of Auxiliary Power Unit (APU) prognostics. To our best knowledge, this is the first application of PF-based method to APU prognostic. The results demonstrated that the PF-based method is useful for estimating TTF for predictive maintenance and it greatly improved TTF estimation precision for APU prognostics.

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Abbreviations

ACARS:

Aircraft Communications Addressing and Reporting System

APU:

Auxiliary Power Unit

EP:

Peak value of exhaust gas temperature

MSE:

Mean Squared Error

NP:

The rotational speed corresponding to EP

PF:

Particle Filtering

RUC:

Remaining Useful Cycles

SIS:

Sequential Important Sampling

SMC:

Sequential Monte Carlo

SMO:

Sequential Minimal Optimization

SVM:

Support Vector Machine

TTF:

Time to Failure

X k :

The vector of system states

Y k :

The measurement of system states

V k :

The noise of measurement

\({x_{k}^{i}}\) :

The i th particle in population

\(\mathrm {w}_{k}^{i}\) :

Impotence weight

N :

Total number of particles

ω k :

Independent Gaussian white noise processes

v :

The standard deviation of RUC

λ :

The starter degradation rate

k :

The steps of SIS sampling process

References

  1. Liu K, Gebraeel N, Shi J (2013) A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Trans Autom Sci Eng 10(3):652–664

    Article  Google Scholar 

  2. You MY, Li L, Meng G, Ni J (2010) Cost-effective updated sequential predictive maintenance policy for continuously monitored degrading systems. IEEE Trans Autom Sci Eng 7(2):581–265

    Google Scholar 

  3. Compare M, Zio E (2014) Predictive maintenance by risk sensitive particle filtering. IEEE Trans Reliab 63(1):134–143

    Article  Google Scholar 

  4. Grall A et al (2002) Continuous time predictive maintenance scheduling for a deteriorating system. IEEE Trans Reliab 51(2):141–150

    Article  Google Scholar 

  5. feng L, Wang H, Si X, Zou H (2013) A state-space-based prognostic model for hidden and age-dependent nonlinear degradation process. IEEE Trans Autom Sci Eng 10(4):1072–1085

    Article  Google Scholar 

  6. Schouten FA, Wartenhorst P Time to failure, time to repair and availability of a two unit standby system with markovian degrading units, Technical report BS-R9007, Center for Mathematics and Computer

  7. Gebraeel N, Lawley M, Liu R, Parmeshwaran V Residual life prediction from vibration-based degradation signals: a neural network approach. IEEE Trans Ind Electron 51(3):694–700

  8. Grottke M, Trivedi K (2005) On a method for mending time to failure distributions. In: Proceedings of International Conference on Dependable Systems and Networks, Los Alamitos, USA, pp 560–569

  9. Promellec B, Riant I, Tessier-Lescourret C (2006) Precise life-time prediction using demarcation energy approximation for distributed activation energy reaction. J Phys Condens Matter 18(2006):2199–2216

    Article  Google Scholar 

  10. Sornette D, Andersen JV (2006) Optimal prediction of time-to-failure from information revealed by damage. Europhys Lett 74(5):778–784

    Article  Google Scholar 

  11. L’etourneau S, Yang C, Drummond C, Scarlett E, Valds J, Zaluski M (2005) A domain independent data mining methodology for prognostics. In: Proceedings of the 59th Meeting of the Society for Machine Failure Prevention Technology, MFPT ’05, Virginia Beach, Virginia, USA

  12. L’etourneau S, Yang C, Liu Z (2008) Improving preciseness of time to failure predictions: application to APU starter. In: Proceedings of the 1st International Conference on Prognostics and Health Management, Denver, Colorado, USA

  13. Yang C, L’etourneau S (2005) Learning to predict train wheel failures. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2005), Chicago, USA, pp 516–525

  14. L’etourneau S, Famili AF, Matwin S (1999) Data mining for prediction of aircraft component replacement. IEEE Intell Syst Appl 14(6):59–66

    Article  Google Scholar 

  15. Hall M (2000) Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the 17th International Conference on Machine Learning, A, pp 359–366

  16. Kira K, Rendell L (1992) A practical approach to feature selection. In: Proceedings of the 9th International Conference on Machine Learning, pp 249–256

  17. Dietterich T (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. IEEE Intell Syst Appl 40:139–158

    Google Scholar 

  18. Tsoumakas IKG, Blahavas I (2004) Effective voting of heterogeneous classifiers. In: Proceedings of the 15th European Conference on Machine Learning (MCML2004), pp 465–476

  19. Dzeroski S, Zenko B (2002) Stacking with multi-response model trees. In: The Proceeding of International Workshop in Multiple Classifier Systems (MCS2002), pp 201–211

  20. Witten IH, Frank E (1999) Data mining: practical machine learning tools and techniques with java implementations. Morgan Kaufmann, Burlington

    Google Scholar 

  21. Dzeroski S, Zenko B (2004) Is combining classifiers with stacking better than selecting the best one? Mach Learn 54:254–273

    Article  MATH  Google Scholar 

  22. Compare M, Zio E (2014) Predictive maintenance by risk sensitive particle filtering. IEEE Trans Reliab 63(1):134–143

    Article  Google Scholar 

  23. You MY, Liu F, Wang W, Meng G (2010) Statistically planned and individually monitored predictive maintenance management for continuously monitored degrading systems. IEEE Trans Reliab 59(4):744–753

    Article  Google Scholar 

  24. Park KS (1993) Condition-based predictive maintenance by multiple logistic function. IEEE Trans Reliab 42(4):556–560

    Article  MATH  Google Scholar 

  25. Zaluski M, Létourneau S, Bird J, Yang C (2011) Developing data mining-based prognostic models for Cf-18 aircraft. J Eng Gas Turbines Power 133(10):1–8

    Article  Google Scholar 

  26. Zaluski M, Létourneau S, Yang C (2009) Data mining-based prognostics for CF18 aircraft components. In: 13th CASI Aeronautics Conference, Kanata, Canada, pp 5–7

  27. Liu J, Wang W, Golnaraghi F (2009) A multi-step predictor with a variable input pattern for system state forecasting. Mech Syst Signal Process 2315:86–99

    Google Scholar 

  28. Gupta S, Ray A (2007) Real-time fatigue life estimation in mechanical structures. Meas Sci Technol 18:1947–1957

    Article  Google Scholar 

  29. Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intell 22:808– 814

    Article  Google Scholar 

  30. Tse P, Atherton D (1999) Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. J Vib Acoust 121:355–362

    Article  Google Scholar 

  31. Adams DE (2002) Nonlinear damage models for diagnosis and prognosis in structural dynamic systems. Proc SPIE 4733:180–191

    Article  Google Scholar 

  32. Luo J et al (2003) An interacting multiple model approach to model-based prognostics. Syst Secur Assurance 1:189– 194

    Google Scholar 

  33. Chelidze D, Cusumano JP (2004) A dynamical systems approach to failure prognosis. J Vib Acoust 126:2–8

    Article  Google Scholar 

  34. Pecht M, Jaai R (2010) A prognostics and health management roadmap for information and electronics-rich systems. Microelectron Reliab 50:317–323

    Article  Google Scholar 

  35. Saha B, Goebel K, Poll S, Christophersen J (2009) Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Trans Instrum Meas 58:291–296

    Article  Google Scholar 

  36. Liu J, Wang W, Golnaraghi F, Liu K (2008) Wavelet spectrum analysis for bearing fault diagnostics. Meas Sci Technol 19:1–9

    Google Scholar 

  37. Liu J, Wang W, Golnaraghi F (2008) An extended wavelet spectrum for baring fault diagnostics. IEEE Trans Instrum Meas 57:2801–2812

    Article  Google Scholar 

  38. Liu J, Wang W, Ma F, Yang Y, Yang C (2012) A data-model-fusion prognostic framework for dynamic system state forecasting. Eng Appl Artif Intell 25(4):814–823

    Article  Google Scholar 

  39. García CM, Chalmers J, Yang C (2012) Particle filter based prognosis with application to auxiliary power unit. In: Proceedings of the Intelligent Monitoring, Control and security f Critical Infrastructure Systems

  40. Doucet GSAC (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Stat Comput, pp 197–208

  41. Arulampalam M, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Trans Sig Proc 50(2):174–188. https://doi.org/10.1109/78.978374

    Article  Google Scholar 

  42. Yang C, Lou Q, Liu J, Yang Y, Bai Y (2014) Particle filter-based method for prognostics with application to auxiliary power unit. In: Proceedings of the International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-2014), Taiwan

  43. Yang C, Lou Q, Liu J, Guo H, Bai Y (2015) Particle filter-based approach to estimate remaining useful life for predictive maintenance. In: Proceedings of the 28th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-2015), Soul, Korea

  44. Yang C, Létourneau S, Liu J, Cheng Q, Yang Y (2016) Machine learning-based methods for TTF estimation with application to APU prognostics. Int J Appl intell 46(1):227–239 . Jan, 2017

    Article  Google Scholar 

  45. Yang C, Zou Y, Lai P, Jiang N (2015) Data mining-based methods for fault isolation with validated FFMEA model ranking. Int J Appl intell 43(4):913–923

    Article  Google Scholar 

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Acknowledgements

We would also like to thank Air Canada for providing the data used in this research. This work is supported by the Natural Science Foundation (Grant No.61463031).

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Authors and Affiliations

  1. National Research Council Canada, Ottawa, Ontario, Canada

    Chunsheng Yang

  2. MicGill University, Montreal, QC, Canada

    Qingfeng Lou

  3. Carleton University, Ottawa, Ontario, Canada

    Jie Liu

  4. Nanjing University, Nanjing, China

    Yubin Yang

  5. Nanchang University, Nanchang, China

    Qiangqiang Cheng

Authors
  1. Chunsheng Yang
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  2. Qingfeng Lou
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  3. Jie Liu
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Corresponding author

Correspondence to Chunsheng Yang.

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Yang, C., Lou, Q., Liu, J. et al. Particle filtering-based methods for time to failure estimation with a real-world prognostic application. Appl Intell 48, 2516–2526 (2018). https://doi.org/10.1007/s10489-017-1083-0

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  • DOI: https://doi.org/10.1007/s10489-017-1083-0

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