Improving data-driven prognostics by assessing predictability of features

Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011, PHM 2011

Volumn , Issue , 2014, Pages 555-560

  Javed, Kamran ^a   Gouriveau, Rafael ^a   Zemouri, Ryad ^b   Zerhouni, Noureddine ^a  

^a FEMTO ST INSTITUTE   (France)

^b Bioinformatique et Chimie Moléculaire   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; FORECASTING; FUZZY INFERENCE; FUZZY SYSTEMS; INFERENCE ENGINES; SYSTEMS ENGINEERING;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; CONDITION-BASED MAINTENANCE; DATA-DRIVEN PROGNOSTICS; FEATURES EXTRACTION; FUZZY C-MEANS ALGORITHMS; LONG-TERM PREDICTION; MULTIDIMENSIONAL DATA; REMAINING USEFUL LIVES;

DATA MINING;

EID: 84920540183     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum New York.
2. Chinnam R.B., & Baruah, P. (2004). A neuro-fuzzy approach for estimating mean residual life in conditionbased maintenance systems. Int. Jour. of Materials & Product Technology, 20, 166-179.
3. Diebold F.X., & Kilian. (2001). Measuring Predictability: Theory and Macroeconomic Applications. Jour. of Applied Econometrics, 16, 657-669.
4. El-Koujok, M., Gouriveau, R., & Zerhouni, N. (2011). Reducing arbitrary choices in model building for prognostics: An approach by applying parsimony principle on an evolving neuro-fuzzy system. Microelectronics Reliability, 51, 310-320.
5. El-Koujok, M., Gouriveau, R., & Zerhouni, N. (2008). Towards a neuro-fuzzy system for time series forecasting in maintenance applications. In IFAC World Congress, Korea.
6. Heng, A., & Zhang, S. (2009). Rotating machinery prognostic: State of the art, challenges and opportunities. Mech. systems & signal processing, 23, 724-739.
7. Jang J.S.R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. Systems, Man, Cybernetics, 23, 665-685.
8. Jardine, A., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. systems & signal processing, 20, 1483-1510.
9. Kaboudan, M. (1999). A Measure of Time-Series Predictability Using Genetic Programming Applied to Stock Returns. Jour. of Forecasting, 18, 345-357.
10. Lebold, M., & Thurston, M. (2001a). Open standards for condition-based maintenance and prognostics systems. In 5th Annual Maint. and Reliability Conf.
11. Lebold, M., & Thurston, M. (2001b). Prognostic Enhancements to diagnostic Systems for Improved Conditionbased maintenance. In Maint. and Reliability Conf. MARCON.
12. Li, C., & Cheng K.H. (2007). Recurrent neuron-fuzzy hybrid-learning approach to accurate system modeling. Fuzzy Sets and Systems, 158, 194-212.
13. Monnet J.-M., & Berger, F. (2010). Support vector machines regression for estimation of forest parameters from airborne laser scanning data. In IEEE IGARSS USA.
14. Ramasso, E., & Gouriveau, R. (2010). Prognostics in Switching Systems: Evidential Markovian Classication of Real-Time Neuro-Fuzzy Predictions. In IEEE Int. Conf. PHM, Hong-Kong.
15. Saxena, A., Celaya, J., Balaban, E., & Saha, B. (2008). Metrics for Evaluating Performance of Prognostic Techniques. In Int. Conf. PHM.
16. Saxena, A., Celaya, J., & Saha, B. (2009). On Applying the Prognostics Performance Metrics. In Annual Conf. of the PHM.
17. Saxena, A., Celaya, J., & Saha, B. (2010). Metrics for Offline Evaluation of Prognostic Performance. Int. Jour. of PHM, 001, 2153-2648.
18. Tobon-Mejia, D., Medjaher, K., & Zerhouni, N. (2011). Estimation of the Remaining Useful Life by using Wavelet Packet Decomposition and HMMs. In IEEE Int. Aerospace Conf., USA (Vol. 6, p. 163-171).
19. Wang W.Q., Goldnaraghi M.F., & Ismail, F. (2004). Prognosis of machine health condition using neuro-fuzzy systems. Mech. systems & signal processing, 18, 813-831.
20. Wang, W., Van Gelder, P. H., & Vrijling J. K. (2008). Measuring predictability of Daily Streamflow Processes Based on Univariate Time Series Model. In iEMSs (Vol. 16, p. 474-3478).
21. Yam R.C.M., Tse P.W., Li, L., & Tu, P. (2001). Intelligent predictive decision support system for condition-based maintenance. Int. Jour. of Adv. Manufacturing Tech., 17, 383-391.