Machine Remaining Useful Life Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering
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
Fatigue Prognosis, Adaptive Neuro-Fuzzy, High-Order Particle Filtering, Bayesian Estimation
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.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.