Abstract
Particle filter (PF)-based method has been widely used for machinery condition-based maintenance (CBM), in particular, for prognostics. It is employed to update the nonlinear prediction model for forecasting system states. In this work, we applied PF techniques to Auxiliary Power Unit (APU) prognostics for estimating remaining useful cycle to effectively perform APU health management. After introducing the PF-based prognostic method and algorithms, the paper presents the implementation for APU Starter prognostics along with the experimental results. The results demonstrated that the developed PF-based method is useful for estimating remaining useful cycle for a given failure of a component or a subsystem.
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Jardine, A., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20, 1483–1510 (2006)
Liu, J., Wang, J.W., Golnaraghi, F.: A multi-step predictor with a variable input pattern for system state forecasting. Mechanical Systems and Signal Processing 2315, 86–99 (2009)
Gupta, S., Ray, A.: Real-time fatigue life estimation in mechanical structures. Measurement Science and Technology 18, 1947–1957 (2007)
Yagiz, S., Gokceoglu, C., Sezer, E., Iplikci, S.: Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Engineering Applications of Artificial Intelligence 22, 808–814 (2009)
Groot, C.D., Wurtz, D.: Analysis of univariate time series with connectionist nets: a case study of two classical cases. Neurocomputing 3, 177–192 (1991)
Tong, H., Lim, K.S.: Threshold autoregression, limited cycles and cyclical data. Journal of the Royal Statistical Society 42, 245–292 (1991)
Subba, R.T.: On the theory of bilinear time series models. Journal of the Royal Statistical Society 43, 244–255 (1981)
Friedman, J.H., Stuetzle, W.: Projection pursuit regression. Journal of the American Statistical Association 76, 817–823 (1981)
Friedman, J.H.: Multivariate adaptive regression splines. Annals of Statistics 19, 1–141 (1981)
Brillinger, D.R.: The identification of polynomial systems by means of higher order spectra. Journal of Sound and Vibration 12, 301–313 (1970)
Atiya, A., El-Shoura, S., Shaheen, S., El-Sherif, M.: A comparison between neural-network forecasting techniques-case study: river flow forecasting. IEEE Transactions on Neural Networks 10, 402–409 (1999)
Liang, Y., Liang, X.: Improving signal prediction performance of neural networks through multi-resolution learning approach. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 36, 341–352 (2006)
Husmeier, D.: Neural networks for conditional probability estimation: forecasting beyond point prediction. Springer, London (1999)
Korbicz, J.: Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer, Berlin (2004)
Wang, W., Vrbanek, J.: An evolving fuzzy predictor for industrial applications. IEEE Transactions on Fuzzy Systems 16, 1439–1449 (2008)
Tse, P., Atherton, D.: Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. Journal of Vibration and Acoustics 121, 355–362 (1999)
Adams, D.E.: Nonlinear damage models for diagnosis and prognosis in structural dynamic systems. In: Proc. SPIE, vol. 4733, pp. 180–191 (2002)
Luo, J., et al.: An interacting multiple model approach to model-based prognostics. System Security and Assurance 1, 189–194 (2003)
Chelidze, D., Cusumano, J.P.: A dynamical systems approach to failure prognosis. Journal of Vibration and Acoustics 126, 2–8 (2004)
Pecht, M., Jaai, R.: A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability 50, 317–323 (2010)
Saha, B., Goebel, K., Poll, S., Christophersen, J.: Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement 58, 291–296 (2009)
Liu, J., Wang, W., Golnaraghi, F., Liu, K.: Wavelet spectrum analysis for bearing fault diagnostics. Measurement Science and Technology 19, 1–9 (2008)
Liu, J., Wang, W., Golnaraghi, F.: An extended wavelet spectrum for baring fault diagnostics. IEEE Transactions on Instrumentation and Measurement 57, 2801–2812 (2008)
Liu, J., Wang, W., Ma, F., Yang, Y.B., Yang, C.: A Data-Model-Fusion Prognostic Framework for Dynamic System State Forecasting. Engineering Applications of Artificial Intelligence 25(4), 814–823 (2012)
García, C.M., Chalmers, J., Yang, C.: Particle Filter Based Prognosis with application to Auxiliary Power Unit. In: The Proceedings of the Intelligent Monitoring, Control and Security of Critical Infrastructure Systems (September 2012)
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 197–208 (2000)
Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Trans. Sig. Proc. 50(2), 174–188 (2002), http://dx.doi.org/10.1109/78.978374
Simon, D.: Optimal State Estimation: Kalman, and Nonlinear Approaches. Wiley Interscience (2006)
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Yang, C., Lou, Q., Liu, J., Yang, Y., Bai, Y. (2014). Particle Filter-Based Method for Prognostics with Application to Auxiliary Power Unit. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_21
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DOI: https://doi.org/10.1007/978-3-319-07455-9_21
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