Physics-based Remaining Useful Life Prediction for Aircraft Engine Bearing Prognosis
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Aircraft engine bearing prognosis not only requires early detection of a bearing defect, but also the ability to predict bearing health conditions for all operational scenarios. This paper summarizes a physics-based remaining useful life (RUL) prediction method developed in the DARP A Engine System Prognosis (ESP) program. This investigation focuses on a typical roller bearing fault (or defect) on the outer raceway. Spall detection is based on the fusion of vibration and online oil debris sensors. Spall size estimation is derived from the amount of bearing debris chips that passed through the Oil Debris Monitor sensor. Subscale propagation tests were performed to generate the response surface of the spall propagation rate under various operating speeds and loads. A particle filter based approach was used to track the spall propagation rate and update the prediction according to newly calculated diagnostics information. The bearing spall propagation model outputs a RUL distribution, which is calculated based on future operating conditions and the time the spall size crossing the failure threshold. The developed RUL prediction method was validated using full- scale bearing spall tests. The comparison of model prediction and measured ground truth demonstrated that the developed model was able to predict the spall propagation rate accurately, and its prediction accuracy and confidence can be further improved by incorporating more diagnostics updates and/or increasing the confidence in the sensor data.
How to Cite
##plugins.themes.bootstrap3.article.details##
aircraft engines, bearings, condition monitoring, damage detection, damage modeling, damage propagation model, data driven prognostics, remaining useful life (RUL), applications: aviation
T. Brotherton. (2000). Prognosis of Faults in Gas Turbine Engines, in Proceedings of IEEE Aerospace Conference, vol. 6, pp. 163-171.
N. Gebraeel, M. Layley, R. Liu, and V. Parmeshwaran. (2004). Residual Life Prediction from Vibration- based Degredation Signals: A Neural Network Approach, IEEE Transactions on Industrial Electronics, vol. 51(3), pp. 694-700.
A.K. Jardine, D. Lin, and D. Banjevic. (2006), A review on machinery diagnostics and prognostics implementing condition based maintenance, Mechanical Systems and Signal Processing, vol. 20(7), pp. 1483-1510.
H. Luo, H. Qiu et al., (2009) Synthesized Synchronous Sampling Technique for Bearing Damage Detection, in Proceedings of the ASME 2009 IDETC/CIE, Aug. 30 – Sep. 2, 2009, San Diego, California, USA.
S. Marble and B. Morton (2005), Predicting the Remaining Life of Propulsion System Bearings, in Proceedings of IEEE Aerospace Conference, Big Sky, MO.
E. Phelps, P. Willett, and T. Kirubarajan. (2001). A statistical approach to prognostics. in Component and Systems Diagnostics, Prognosis and Health Management, vol. 4389, Bellingham pp. 23-34.
P.J. Vlok, M. Wnek, and M. Zygmunt. (2004). Utilizing statistical residual life estimates of bearings to quantify the influence of preventative maintenance actions. Mechanical Systems and Signal Processing, vol. 18, pp. 833-847.
R.A. Wade. (2005), A Need-focused Approach to Air Force Engine Health Management Research, in Proceedings of IEEE Aerospace Conference, Big Sky, MO.
W. Wang. (2002). A model to predict the residual life of rolling element bearings given monitored condition information to date, IMA Journal of Management Mathematics, vol. 13, pp. 3-16.
W.Q. Wang, M.F. Golnaraghi, and F. Ismail. (2004). Prognosis of machine health condition using neuro- fuzzy systems, Mechanical Systems and Signal Processing, vol. 18. pp. 813-831
R.M. Yam, P.W. Tse, L. Li, and P. Tu. (2001). Intelligent predictive decision support system for condition based maintenance, International Journal of Advanced Manufacturing Technology, pp. 383- 391.
S. Zhang, and R. Ganesan. (1997). Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery, ASME Journal of Engineering for Gas Turbines and Power, vol. 119, pp. 378-384.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
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.