Prognostics model for predicting aero-engine bearing grade-life

Proceedings of the ASME Turbo Expo

Volumn 1, Issue , 2009, Pages 639-647

  Hong, Jie ^a   Miao, Xuewen ^a   Han, Lei ^a   Ma, Yanhong ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

Aeroengine bearing; Grade life; Grade life model; SVM, fuzzy logic inference

Indexed keywords

ACCELERATED DEGRADATION; AERO-ENGINE; ANALYTIC MODELS; BEARING TESTS; CUMULATIVE DAMAGE; ENGINE BEARING; ESTIMATE MODEL; FAILURE DATA; FLIGHT HOUR; FUZZY LOGIC INFERENCE; GAS TURBINE ENGINE; LIFE ASSESSMENT; LIFE MODELS; LIFETIME MODELS; OPERATING CONDITION; PHYSICS-BASED; PROGNOSTIC APPROACH; PROGNOSTIC MODEL; VIBRATION SIGNAL;

AIRCRAFT ENGINES; BEARINGS (STRUCTURAL); ENGINES; FORECASTING; FUZZY INFERENCE; FUZZY SYSTEMS; GAS TURBINES;

MATHEMATICAL MODELS;

EID: 77953183536     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/GT2009-59641     Document Type: Conference Paper

References (13)

1. Wan Changsen. Rolling Bearing Analysis [M]. Beijing: Mechanism Industry Publishing Company, 1987(in Chinese).
2. Harris, T. (4th Edition 2001), Rolling Bearing Analysis,John Wiley & Sons, New York.
3. Tallian T E. "Weibull Distribution of Rolling Contact Fatigue Life and Deviations Therefrom," ASLE Trans, 1962, V5(1): 103-113.
4. International Organization for Standardization, Rolling Bearings Dynamic Load Ratings and Rating Life," ISO281/I. 1977(E).
5. Williams T., Ribadeneira X.and Billingto S. Rolling element bearing diagnostics in run-to-failure life time testing [J]. Mechanical Systems and Signal Processing, 2001,V1 15(5): 979-993.
6. Mark Lawley, R. Liu, and Vijay Parmeshwaran. Residual Life Predictions From Vibration-Based Degradation Signals: A Neural Network Approach [J], IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2004,V51(3): 694-699.
7. Runqing Huang, Lifeng Xia, Xinglin Li, ect. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing , 2007 (21):193-207
8. Advanced Engineering Informatics, 2003 (17): 127-140.
9. Hai Qiu, Jay Lee, Jing Lin, Gang Yu. Robust performance degradation assessment methods for enhanced rolling element bearing prognostics[J]. Advanced Engineering Informatics, 2003 (17): 127-140.
10. Y Shao, K Nezu. Prognosis of remaining bearing life using neural networks[J]. Proc Instn Mech Engrs, 2000, 214 Part I: 217:231.
11. Vapnik V N. The nature of statistical learning theory[M].2nd edition, New York: Springer-Verlag,2000.
12. Cherkassky V, MA Yunqian. Practical selection of SVM parameters and noise estimation for SVM regression [J]. Neural Networks, 2004,17(1): 113-126. (Pubitemid 38019003)
13. Cherkassky V, MA Yunqian. Selection of meta-parameters for support vector regression [C]//Proceedings of ICANN2002. Verlag Berlin Heidelberg: Springer, 2002: 687-693.