Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on remaining useful life estimation results. A similarity-based prognostic model is modified to employ the most similar training samples for RUL estimations on each time instance. The presented model is tested on; Virkler’s fatigue crack growth dataset, a drilling process degradation dataset, and a sliding chair degradation of a turnout system dataset. Prediction performances are compared utilizing an evaluation metric. Efficiency analysis of optimization results show that the modified similarity-based model performs better than the original definition.
How to Cite
Data-driven prognostics, anomaly detection, similarity-based modelling, multivariate analysis
Byington, C. S., Watson, M., Edwards, D. and Stoelting, P. (2004), "A model-based approach to prognostics and health management for flight control actuators", IEEE Aerospace Conference Proceedings, Vol. 6, pp. 3551.
Camci, F., Eker, O. F., Baskan, S. and Konur, S. (2014), "Comparison of sensors and methodologies for effective prognostics on railway turnout systems", Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit.
Camci, F. (2005), Process monitoring, diagnostics and prognostics using support vector machines and hidden Markov models (PhD thesis), Graduate School of Wanye State University, Detroit, Lambert Academic Publishing.
Camci, F. and Chinnam, R. B. (2010), "Health-state estimation and prognostics in machining processes", IEEE Transactions on Automation Science and Engineering, vol. 7, no. 3, pp. 581-597.
Camci et al., (2010), http://www.aiu.edu.tr/staff/fatih.camci/datasets.html
Chinnam et al. (2003), Project Name: 'Diagnostic and Prognostic Algorithms for Condition-Based-Maintenance'. Principal investigator: Ratna B. Chinnam, Funded by Advanced Manufacturing Technology Development (AMTD) group of Ford Motor Company for $65,000, 2003-2004.
Eker, O. F., Camci, F., Guclu, A., Yilboga, H., Sevkli, M. and Baskan, S. (2011), "A simple state-based prognostic model for railway turnout systems", IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp. 1718-1726.
Ertunc, H. M., Loparo, K. A., Ozdemir, E. and Ocak, H. (2001), "Real time monitoring of tool wear using multiple modeling method", Electric Machines and Drives Conference, 2001. IEMDC 2001. IEEE International, pp. 687.
Furness, R. J., Tsu-Chin Tsao, Rankin, J. S.,II, Muth, M. J. and Manes, K. W. (1999), "Torque control for a form tool drilling operation", Control Systems Technology, IEEE Transactions on, vol. 7, no. 1, pp. 22-30.
Gebraeel, N. Z. and Lawley, M. A. (2008), "A neural network degradation model for computing and updating residual life distributions", IEEE Transactions on Automation Science and Engineering, vol. 5, no. 1, pp. 154-163.
Guclu, A., Yılboga, H., Eker, O. F., Camci, F. and Jennions, I. (2010), "Prognostics with Autoregressive Moving Average for Railway Turnouts", Annual Conference of Prognostics and Health Management Society, Portland, Oregon, USA, 10-14 October 2010, .
Kacprzynski, G. J., Roemer, M. J., Modgil, G., Palladino, A. and Maynard, K. (2002), "Enhancement of physics-of-failure prognostic models with system level features", Aerospace Conference Proceedings, 2002. IEEE, Vol. 6, pp. 6-2919.
Lianyu Fu and Ling, S. -. (2002), "Neural network based on-line detection of drill breakage in micro drilling process", Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on, Vol. 4, pp. 2054.
Qiu, J., Seth, B. B., Liang, S. Y. and Zhang, C. (2002), "Damage mechanics approach for bearing lifetime prognostics", Mechanical Systems and Signal Processing, vol. 16, no. 5, pp. 817-829.
Saha, S., Saha, B., Saxena, A. and Goebel, K. (2010), "Distributed prognostic health management with gaussian process regression", Aerospace Conference, 2010 IEEE, pp. 1.
Si, X. -., Wang, W., Hu, C. -. and Zhou, D. -. (2011), "Remaining useful life estimation - A review on the statistical data driven approaches", European Journal of Operational Research, vol. 213, no. 1, pp. 1-14.
Virkler, D. A., Hillberry, B. M. and Goel, P. K. (1979), "The Statistical Nature of Fatigue Crack Propagation", vol. 101, no. 2, pp. 148-153.
Zhang, H., Kang, R. and Pecht, M. (2009), "A hybrid prognostics and health management approach for condition-based maintenance", IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1165.
Zio, E. and Di Maio, F. (2010), "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system", Reliability Engineering & System Safety, vol. 95, no. 1, pp. 49-57.
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