Improving data-driven prognostics by assessing predictability of features



Published Sep 25, 2011
Kamran Javed Rafael Gouriveau Ryad Zemouri Noureddine Zerhouni


Within condition based maintenance (CBM), the whole aspect of prognostics is composed of various tasks from multidimensional data to remaining useful life (RUL) of the equipment. Apart from data acquisition phase, data-driven prognostics is achieved in three main steps: features extraction and selection, features prediction, and health-state classification. The main aim of this paper is to propose a way of improving existing data-driven procedure by assessing the predictability of features when selecting them. The underlying idea is that prognostics should take into account the ability of a practitioner (or its models) to perform long term predictions. A predictability measure is thereby defined and applied to temporal predictions during the learning phase, in order to reduce the set of selected features. The proposed methodology is tested on a real data set of bearings to analyze the effectiveness of the scheme. For illustration purpose, an adaptive neuro-fuzzy inference system is used as a prediction model, and classification aspect is met by the well known Fuzzy C- means algorithm. Both enable to perform RUL estimation and results appear to be improved by applying the proposed strategy.

How to Cite

Javed, K. ., Gouriveau, R. ., Zemouri, R. ., & Zerhouni, N. . (2011). Improving data-driven prognostics by assessing predictability of features. Annual Conference of the PHM Society, 3(1).
Abstract 185 | PDF Downloads 130



data driven prognostics, RUL prediction, predictability

Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum New York.

Chinnam R.B., & Baruah, P. (2004). A neuro-fuzzy approach for estimating mean residual life in condition- based maintenance systems. Int. Jour. of Materials & Product Technology, 20, 166-179.

Diebold F.X., & Kilian. (2001). Measuring Predictability: Theory and Macroeconomic Applications. Jour. of Applied Econometrics, 16, 657-669.

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.

El-Koujok, M., Gouriveau, R., & Zerhouni, N. (2008). To- wards a neuro-fuzzy system for time series forecasting in maintenance applications. In IFAC World Congress, Korea.

Heng, A., & Zhang, S. (2009). Rotating machinery prognostic: State of the art, challenges and opportunities. Mech. systems & signal processing, 23, 724-739.

Jang J.S.R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. Systems, Man, Cybernetics, 23, 665-685.

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.

Kaboudan, M. (1999). A Measure of Time-Series Predictability Using Genetic Programming Applied to Stock Returns. Jour. of Forecasting, 18, 345-357.

Lebold, M., & Thurston, M. (2001a). Open standards for condition-based maintenance and prognostics systems. In 5th Annual Maint. and Reliability Conf.

Lebold, M., & Thurston, M. (2001b). Prognostic Enhancements to diagnostic Systems for Improved Condition- based maintenance. In Maint. and Reliability Conf. MARCON.

Li, C., & Cheng K.H. (2007). Recurrent neuron-fuzzy hybrid-learning approach to accurate system modeling. Fuzzy Sets and Systems, 158, 194-212.

Monnet J.-M., & Berger, F. (2010). Support vector machines regression for estimation of forest parameters from airborne laser scanning data. In IEEE IGARSS USA.

Ramasso, E., & Gouriveau, R. (2010). Prognostics in Switching Systems: Evidential Markovian Classification of Real-Time Neuro-Fuzzy Predictions. In IEEE Int. Conf. PHM, Hong-Kong.

Saxena, A., Celaya, J., Balaban, E., & Saha, B. (2008). Metrics for Evaluating Performance of Prognostic Techniques. In Int. Conf. PHM.

Saxena, A., Celaya, J., & Saha, B. (2009). On Applying the Prognostics Performance Metrics. In Annual Conf. of the PHM.

Saxena, A., Celaya, J., & Saha, B. (2010). Metrics for Offline Evaluation of Prognostic Performance. Int. Jour. of PHM, 001, 2153-2648.

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).

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.

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).

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.
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