Machine Remaining Useful Life Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering



Chaochao Chen George Vachtsevanos Marcos E. Orchard


Machine remaining useful life (RUL) prediction is a key part of Condition-Based Maintenance (CBM), which provides the time evolution of the fault indicator so that maintenance can be performed to avoid catastrophic failures. This paper proposes a new RUL prediction method based on adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which predicts the time evolution of the fault indicator and computes the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter to describe the fault propagation process; the high-order particle filter uses real-time data to update the current state estimates so as to improve the prediction accuracy. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results show that it outperforms the conventional ANFIS predictor.

How to Cite

Chen, C. ., Vachtsevanos, G. ., & E. Orchard, M. (2010). Machine Remaining Useful Life Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering. Annual Conference of the PHM Society, 2(1).
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Fatigue Prognosis, Adaptive Neuro-Fuzzy, High-Order Particle Filtering, Bayesian Estimation

Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A. & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 1st ed. Hoboken, New Jersey: John Wiley & Sons, Inc,.

Gebraeel, N., Lawley, M., Liu, R., Parmeshwaran, V. (2004). Residual life predictions from vibration- based degradation signals: a neural network approach, IEEE Transactions on Industrial Electronics, vol. 51, pp. 694-700.

Tse, P., Atherton, D. (1999). Prediction of machine deterioration using vibration based fault trends and recurrent neural networks, Journal of Vibration and Acoustics, vol. 121, pp.355-362.

Zhao, F., Chen, J., Guo, L., Lin, X. (2009). Neuro fuzzy based condition prediction of bearing health, Journal of Vibration and Control, vol. 15, pp. 1079- 1091.

Wang, W., Golnaraghi, F., Ismail, F. (2004). Prognosis of machine health condition using neuro-fuzzy systems, Mechanical System and Signal Processing, vol. 18, pp. 813-831.

Liu, J., Wang, W., Golnaraghi, F. (2009). A multi-step predictor with a variable input pattern for system state forecasting, Mechanical System and Signal Processing, vol. 23, pp. 1586-1599.

Wang, P., Vachtsevanos, G. (2001). Fault prognostics using dynamic wavelet neural networks, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol. 15, pp. 349-365.

Samanta, B., Nataraj, C. (2008). Prognostics of machine condition using soft computing, Robotics and Computer-Integrated Manufacturing., vol. 24, pp. 816-823.

Tran, V., Yang, B., Tan, A. (2009). Multi-step ahead direct prediction for machine condition prognosis using regression trees and neuro-fuzzy systems, Expert System with Application, vol. 36, pp. 9378- 9387.

Wang, W. (2007). An adaptive predictor for dynamic system forecasting, Mechanical System and Signal Processing, vol. 21, pp. 809-823.

Jardine, A.K.S., Lin, D., Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical System and Signal Processing, vol. 20, pp. 1483-1510.

Arulampalam, M.S., Maskell, S., Gordon, N., Clapp T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing., vol. 50, pp.174–188.

Orchard, M., and Vachtsevanos, G. (2009). “A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis,” Transactions of the Institute of Measurement and Control, vol. 31, no. 3-4, pp. 221- 246.

Jang, J. -S. R., Sun, C. -T., Mizutani, E. (1997). Neuro- Fuzzy and Soft Computing. NJ: Prentice-Hall PTR.

Whittaker, J. (1990) Graphical Models in Applied Mathematical Multivariate Statistics, United Kingdom: John Wiley & Sons.

Tian, Z. Wong, L. Safaei, N. (2009). A neural network approach for remaining useful life prediction utilizing both failure and suspension histories, Mechanical System and Signal Processing doi:10.1016/j.ymssp.2009.11.005.

Zhang, B. Khawaja, T. Patrick, R. Vachtsevanos, G. (2008). Blind deconvolution denoising for helicopter vibration signals, IEEE/ASME Transactions on Mechatronics, vol. 13, pp. 558-565.
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