Segmentation Based Feature Evaluation and Fusion for Prognostics



Published Nov 16, 2020
Vepa Atamuradov Fatih Camci


Quantification of feature goodness, called feature evaluation, is crucial in the identification of best features and achieving high accuracy in diagnostics and prognostics. Even though feature evaluation for diagnostics is a mature area, it is a developing research area for prognostics. The feature goodness for prognostics is measured by change in degradation. Most, if not all, of existing methods, analyze the feature change in the whole failure degradation. In other words, features collected throughout the failure degradation are analyzed to create a goodness value for the feature. In reality, the goodness of the features may change during the failure progression. A feature may be a good representative of failure progression in the initial phase but not in the final phases, or vice versa. This paper presents a methodology that divides the features into segments, each of which may have different goodness for prognostics. Thus, some part of the feature may be good, whereas the others not. The presented approach leads to extract more value from the features’ changing properties during the failure degradation. The method has been applied to simulated and real datasets obtained from Li-ion batteries aging tests. State of health (SoH) estimation accuracy is enhanced with the presented approach.

Abstract 166 | PDF Downloads 181



Feature Selection, Feature Evaluation, Time Series Segmentation, Feature fusion, SoH estimation, Prognostics, Remaining Useful Life

Aizpurua, J. I., & Catterson, V. M. (2015). Towards a Methodology for Design of Prognostic Systems. Annual Conference of the Prognostics and Health Management Society, (October), 1–13.
Atamuradov, V., & Camci, F. (2016). Evaluation of Features with Changing Effectiveness for Prognostics. Annual Annual Conference of the Prognostics and Health Management Society.
Camci, F., & Chinnam, R. B. (2005). Dynamic Bayesian networks for machine diagnostics: Hierarchical Hidden Markov models vs. competitive learning. Proceedings of the International Joint Conference on Neural Networks, 3, 1752–1757.
Camci, F., Medjaher, K., Zerhouni, N., & Nectoux, P. (2013). Feature Evaluation for Effective Bearing Prognostics. Quality and Reliability Engineering International, 29(4), 477–486.
Camci, F., Ozkurt, C., Toker, O., & Atamuradov, V. (2015). Sampling based State of Health estimation methodology for Li-ion batteries. Journal of Power Sources, 278, 668–674.
Cecille, F., Dana, K., & B;, O. (2015). An evaluation of classifier-specific filter measure performance for feature selection. Pattern Recognition, 48(5), 1812–1826.
Coble, J. (2009). An Automated Approach for Fusing Data Sources to Identify Optimal Prognostic Parameters. Annual Conference of the Prognostics and Health Management Society.
Coble, J. B. (2010). Merging data sources to predict remaining useful life--an automated method to identify prognostic parameters. Retrieved from
Coble, J., & Hines, J. W. (2009). Identifying optimal prognostic parameters from data: a genetic algorithms approach. Proceedings of the Annual Conference of the Prognostics and Health Management Society, 1–11.
Eker, O. F., & Camci, F. (2013). State Based Prognostics with State Duration Information,. Quality Reliability Engineering International, 29(4), 465–476.
Eker, O. F., Camci, F., & Jennions, I. K. (2015). Physics-based prognostic modelling of filter clogging phenomena. Mechanical Systems and Signal Processing, 75, 395–412.
Fang, X., Paynabar, K., & Gebraeel, N. (2017). Multistream sensor fusion-based prognostics model for systems with single failure modes. Reliability Engineering and System Safety, 159(November 2016), 322–331.
Gelman, L., Patel, T. H., Persin, G., Murray, B., & Thomson, A. (2013). Novel Technology Based on the Spectral Kurtosis and Wavelet Transform for Rolling Bearing Diagnosis. Int. J. Progn. Health Manag, 4(2), 1–7.
Glezakos, T., Tsiligiridis, T. A., & Yialouris, C. P. (2014). Piecewise evolutionary segmentation for feature extraction in time series models. Neural Computing and Applications, 24(2), 243–257.
Guana, D., Yuana, W., Leea, Y. K., Najeebullaha, K., & Rasela, M. K. (2014). A Review of Ensemble Learning Based Feature Selection. IETE Technical Review, 31(3), 190–198.
Hannah Inbarani, H., Bagyamathi, M., & T;, A. A. (2015). A novel hybrid feature selection method based on rough set and improved harmony search. Neural Computing and Applications, 26(8), 1859–1880.
Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10.
Honeine, P. (2012). Online Kernel Principal Component Analysis: A Reduced-Order Model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 1814–1826.
Javed, K., Gouriveau, R., Zerhouni, N., & Nectoux, P. (2015). Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics. IEEE Transactions on Industrial Electronics, 62(c), 647–656.
Lamraoui, M., Barakat;, M., Thomas, M., & Badaoui, M. El. (2015). Chatter detection in milling machines by neural network classification and feature selection. Journal of Vibration and Control, 21(7), 1251–1266.
Liang, L., Liu, F., Li, M., He, K., & Xu, G. (2016). Feature selection for machine fault diagnosis using clustering of non-negation matrix factorization. Measurement, 94, 295–305.
Linxia, L. (2014). Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction. IEEE Transactions on Industrial Electronics, , 61(5), 2464–2472.
Liu, L., Wang, S., Liu, D., Zhang, Y., & Peng, Y. (2015). Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine. Microelectronics Reliability, 55(9–10), 2092–2096.
Lui, J., Zhang, M., Zuo, H., & Xie, J. (2014). Remaining useful life prognostics for aeroengine based on superstatistics and information fusion. Chinese Journal of Aeronoutics, 1086–1096.
Lui, K., Gabraeel, N. Z., & Shi, J. (2013). A data-level fusioin model for developing composite health indices for degradation modeling and prognostics analysis. Automation Science and Engineering, IEEE Transactions, 10(3), 652–664.
Maia, A. L. S., & de Carvalho, F. de A. T. (2011). Holt’s exponential smoothing and neural network models for forecasting interval-valued time series. International Journal of Forecasting, 27(3), 740–759.
Mwangi, B., Tian, T. V., & Soares, J. C. (2014). A Review of Feature Reduction Techniques in Neuroimaging. Neuroinformatics, 12(2), 229–244.
Peng, Y., Xu, Y., Liu, D., & Li, J. (2015). Locality structure preserving based feature selection for prognostics. Intelligent Data Analysis, 19(3), 659–682.
Qu, Y., Bechhoefer, E., He, D., & Zhu, J. (2013). A New Acoustic Emission Sensor Based Gear Fault Detection Approach. Int. J. Progn. Health Manag, 4, 1–14.
Saha, B., Goebel, K., & Christophersen, J. (2009). Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement and Control, 31(3–4), 293–308.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for Offline Evaluation of Prognostic Performance. Int. J. Progn. Health Manag.
Senoussi, H., & Chebel-Morello, B. (2011). Feature selection and categorization to design reliable fault detection systems. Control.
Tianzhen, W., Hao, X., & Jingang, H. (2015). Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach. IEEE Transactions on Power Electronics, 30(12), 7006–7018.
Wang, L., Zhang, J., Yin, J., & Liu, H. (2014). Global and Local Structure Preservation for Feature Selection. IEEE Transactions on Neural Networks and Learning Systems, 25(6), 1083–1095.
Williard, N., He, W., Osterman, M., & Pecht, M. (2013). Comparative analysis of features for determining state of health in lithium-ion batteries. Int. J. Progn. Health Manag, 2013(4), 1–7.
Yan, H., Liu, K., Zhang, X., & Shi, J. (2016). Multiple Sensor Data Fusion for Degradation Modeling and Prognostics under Multiple Operational Conditions. IEEE Transactions on Reliability, 65(3), 1416–1426.
Z, W., Jin, C., & Guangming, D. (2011). Constrained independent component analysis and its application to machine fault diagnosis. Mechanical Systems and Signal Processing, 25(7), 2501–2512.
Zhang, J., & Lee, J. (2011). A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 196(15), 6007–6014.
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