A New Hybrid Prognostic Methodology
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Published
Jun 1, 2019
Omer F. Eker
Fatih Camci
Ian K. Jennions
Abstract
Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications, this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason, a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer-term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available.
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Keywords
empirical model, physical modeling, hybrid algorithms, similarity-based modelling
References
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Baruah, P. and Chinnam, R. B. (2005), "HMMs for diagnostics and prognostics in machining processes", International Journal of Production Research, vol. 43, no. 6, pp. 1275-1293.
Butler, S. and Ringwood, J. (2010), "Particle filters for remaining useful life estimation of abatement equipment used in semiconductor manufacturing", Control and Fault-Tolerant Systems (SysTol), 2010 Conference on, pp. 436.
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Daigle, M. and Goebel, K. (2010), "Model-based prognostics under limited sensing", IEEE Aerospace Conference Proceedings, .
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Daigle, M., (2014), Model based prognostics (Tutorial), PHM Society.
Eker O. F. (2015), A Hybrid Prognostic Methodology and its Application to Well-Controlled Engineering Systems (PhD thesis), Cranfield University.
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Fan, J., Yung, K. and Pecht, M. (2015), "Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach", Expert Systems with Applications, vol. 42, no. 5, pp. 2411-2420.
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.
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Marjanovic, A., Kvascev, G., Tadic, P. and Djurovic, Z. (2011), "Applications of predictive maintenance techniques in industrial systems", Serbian Journal of Electrical Engineering, vol. 8, no. 3, pp. 263-279.
Paris, P. C. and Erdogan, F. (1963), "A critical analysis of crack propagation laws", Journal of Basic Engineering, Trans. ASME, vol. Ser. D, no. 85, pp. 528-534.
Peng, Y., Dong, M. and Zuo, M. J. (2010), "Current status of machine prognostics in condition-based maintenance: A review", International Journal of Advanced Manufacturing Technology, vol. 50, no. 1-4, pp. 297-313.
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Saha, B., Celaya, J. R., Wysocki, P. F. and Goebel, K. F. (2009), "Towards prognostics for electronics components", Aerospace conference, 2009 IEEE, pp. 1.
Samie, M., Perinpanayagam, S., Alghassi, A., Motlagh, A. and Kapetanios, E., (2014), Developing Prognostic Models Using Duality Principles for DC-to-DC Converters.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S. and Schwabacher, M. (2008), "Metrics for evaluating performance of prognostic techniques", 2008 International Conference on Prognostics and Health Management, PHM 2008,
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Sikorska, J. Z., Hodkiewicz, M. and Ma, L. (2011), "Prognostic modelling options for remaining useful life estimation by industry", Mechanical Systems and Signal Processing, vol. 25, no. 5, pp. 1803-1836.
Tien, C. and Ramarao, B. V. (2013), "Can filter cake porosity be estimated based on the Kozeny-Carman equation?", Powder Technology, vol. 237, pp. 233-240.
Vachtsevanos, G. J. and Valavanis, K. P. (2009), Applications of Intelligent Control to Engineering Systems: In Honour of Dr. G. J. Vachtsevanos, Springer.
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.
Wang D., K. Tsui and Q. Miao (2018), "Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators," in IEEE Access, vol. 6, pp. 665-676, 2018
Wang, T. (2010), Trajectory Similarity Based Prediction for Remaining Useful Life Estimation (PhD thesis), University Of Cincinnati
Wang, W. and Carr, M. (2010), "An adapted Brownion motion model for plant residual life prediction", 2010 Prognostics and System Health Management Conference, PHM '10, .
Wu J., C. Wu, S. Cao, S. W. Or, C. Deng and X. Shao, (2018) "Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines," in IEEE Transactions on Industrial Electronics, vol. PP, no. 99, pp. 1-1.
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.
Zhang, L., Li, X. and Yu, J. (2006), "A review of fault prognostics in condition based maintenance", Proceedings of SPIE - The International Society for Optical Engineering, Vol. 6357 II, .
Zhao F., Z. Tian, X. Liang and M. Xie (2018), "An Integrated Prognostics Method for Failure Time Prediction of Gears Subject to the Surface Wear Failure Mode," in IEEE Transactions on Reliability, vol. 67, no. 1, pp. 316-327.
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.
Zio, E. and Peloni, G. (2011), "Particle filtering prognostic estimation of the remaining useful life of nonlinear components", Reliability Engineering & System Safety, vol. 96, no. 3, pp. 403-409.
Baraldi, P., Compare, M., Sauco, S. and Zio, E. (2012), "Fatigue Crack Growth Prognostics by Particle Filtering and Ensemble Neural Networks", 1st european conference of the prognostics and health management society 2012, Vol. 3, 2012, Dresden, Germany, PHM Society, Dresden, Germany.
Baruah, P. and Chinnam, R. B. (2005), "HMMs for diagnostics and prognostics in machining processes", International Journal of Production Research, vol. 43, no. 6, pp. 1275-1293.
Butler, S. and Ringwood, J. (2010), "Particle filters for remaining useful life estimation of abatement equipment used in semiconductor manufacturing", Control and Fault-Tolerant Systems (SysTol), 2010 Conference on, pp. 436.
Cadini, F., Zio, E. and Avram, D. (2009), "Monte Carlo-based filtering for fatigue crack growth estimation", Probabilistic Engineering Mechanics, vol. 24, no. 3, pp. 367-373.
Daigle, M. and Goebel, K. (2010), "Model-based prognostics under limited sensing", IEEE Aerospace Conference Proceedings, .
Daigle, M. J. and Goebel, K. (2013), "Model-Based Prognostics With Concurrent Damage Progression Processes", Systems, Man, and Cybernetics: Systems, IEEE Transactions on, vol. 43, no. 3, pp. 535-546.
Daigle, M., (2014), Model based prognostics (Tutorial), PHM Society.
Eker O. F. (2015), A Hybrid Prognostic Methodology and its Application to Well-Controlled Engineering Systems (PhD thesis), Cranfield University.
Eker O. F., Camci F., and Jennions I. K., Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets, PHM Conference Europe, Dresden Germany, July 2012
Eker O.F., Camci F., Jennions I.K. (2016), Physics-based prognostic modelling of filter clogging phenomena, Mechanical Systems and Signal Processing, Volume 75, 15, Pages 395-412, ISSN 0888-3270, http://dx.doi.org/10.1016/j.ymssp.2015.12.011.
Fan, J., Yung, K. and Pecht, M. (2015), "Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach", Expert Systems with Applications, vol. 42, no. 5, pp. 2411-2420.
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.
Goebel K., N. Eklund, and P. Bonanni (2006), “Fusing competing prediction algorithms for prognostics,” in Proc. IEEE Aerospace Conf., 2006, p.10.
Goebel K., N. Eklund, and P. Bonanni (2007), Prognostic Fusion for Uncertainty Reduction. Wright-Patterson AFB, OH, USA: Defense Technical, 2007
Hecht, H. (2006), "Why prognostics for avionics?", IEEE Aerospace Conference Proceedings, Vol. 2006.
Heng, A., Zhang, S., Tan, A. C. C. and Mathew, J. (2009), "Rotating machinery prognostics: State of the art, challenges and opportunities", Mechanical Systems and Signal Processing, vol. 23, no. 3, pp. 724-739.
Huang, R., Xi, L., Li, X., Richard Liu, C., Qiu, H. and Lee, J. (2007), "Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods", Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 193-207.
Huang, R., Xi, L., Li, X., Richard Liu, C., Qiu, H. and Lee, J. (2007), "Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods", Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 193-207.
Jardine, A. K. S., Lin, D. and Banjevic, D. (2006), "A review on machinery diagnostics and prognostics implementing condition-based maintenance", Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483-1510.
Jouin, M., Gouriveau, R., Hissel, D., Péra, M. and Zerhouni, N. (2014), "Prognostics of PEM fuel cell in a particle filtering framework", International Journal of Hydrogen Energy, vol. 39, no. 1, pp. 481-494.
Kothamasu, R., Huang, S. H. and Verduin, W. H. (2006), "System health monitoring and prognostics - A review of current paradigms and practices", International Journal of Advanced Manufacturing Technology, vol. 28, no. 9, pp. 1012-1024.
Kwan, C., Zhang, X., Xu, R. and Haynes, L. (2003), "A novel approach to fault diagnostics and prognostics", Proceedings - IEEE International Conference on Robotics and Automation, Vol. 1, pp. 604.
Liao, L. and Kottig, F. (2014), "Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction", Reliability, IEEE Transactions on, vol. 63, no. 1, pp. 191-207.
Liu, H., Shang, D., Liu, J. and Guo, Z. (2015), "Fatigue life prediction based on crack closure for 6156 Al-alloy laser welded joints under variable amplitude loading", International Journal of Fatigue, vol. 72, no. 0, pp. 11-18.
Marjanovic, A., Kvascev, G., Tadic, P. and Djurovic, Z. (2011), "Applications of predictive maintenance techniques in industrial systems", Serbian Journal of Electrical Engineering, vol. 8, no. 3, pp. 263-279.
Paris, P. C. and Erdogan, F. (1963), "A critical analysis of crack propagation laws", Journal of Basic Engineering, Trans. ASME, vol. Ser. D, no. 85, pp. 528-534.
Peng, Y., Dong, M. and Zuo, M. J. (2010), "Current status of machine prognostics in condition-based maintenance: A review", International Journal of Advanced Manufacturing Technology, vol. 50, no. 1-4, pp. 297-313.
Peng, Y., Y. Wang and Y. Zi, (2018) "Switching state-space degradation model with recursive filter/smoother for prognostics of remaining useful life," in IEEE Transactions on Industrial Informatics, vol. PP, no. 99, pp. 1-1.
Saha, B., Celaya, J. R., Wysocki, P. F. and Goebel, K. F. (2009), "Towards prognostics for electronics components", Aerospace conference, 2009 IEEE, pp. 1.
Samie, M., Perinpanayagam, S., Alghassi, A., Motlagh, A. and Kapetanios, E., (2014), Developing Prognostic Models Using Duality Principles for DC-to-DC Converters.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S. and Schwabacher, M. (2008), "Metrics for evaluating performance of prognostic techniques", 2008 International Conference on Prognostics and Health Management, PHM 2008,
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.
Sikorska, J. Z., Hodkiewicz, M. and Ma, L. (2011), "Prognostic modelling options for remaining useful life estimation by industry", Mechanical Systems and Signal Processing, vol. 25, no. 5, pp. 1803-1836.
Tien, C. and Ramarao, B. V. (2013), "Can filter cake porosity be estimated based on the Kozeny-Carman equation?", Powder Technology, vol. 237, pp. 233-240.
Vachtsevanos, G. J. and Valavanis, K. P. (2009), Applications of Intelligent Control to Engineering Systems: In Honour of Dr. G. J. Vachtsevanos, Springer.
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.
Wang D., K. Tsui and Q. Miao (2018), "Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators," in IEEE Access, vol. 6, pp. 665-676, 2018
Wang, T. (2010), Trajectory Similarity Based Prediction for Remaining Useful Life Estimation (PhD thesis), University Of Cincinnati
Wang, W. and Carr, M. (2010), "An adapted Brownion motion model for plant residual life prediction", 2010 Prognostics and System Health Management Conference, PHM '10, .
Wu J., C. Wu, S. Cao, S. W. Or, C. Deng and X. Shao, (2018) "Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines," in IEEE Transactions on Industrial Electronics, vol. PP, no. 99, pp. 1-1.
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.
Zhang, L., Li, X. and Yu, J. (2006), "A review of fault prognostics in condition based maintenance", Proceedings of SPIE - The International Society for Optical Engineering, Vol. 6357 II, .
Zhao F., Z. Tian, X. Liang and M. Xie (2018), "An Integrated Prognostics Method for Failure Time Prediction of Gears Subject to the Surface Wear Failure Mode," in IEEE Transactions on Reliability, vol. 67, no. 1, pp. 316-327.
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.
Zio, E. and Peloni, G. (2011), "Particle filtering prognostic estimation of the remaining useful life of nonlinear components", Reliability Engineering & System Safety, vol. 96, no. 3, pp. 403-409.
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Technical Papers