Validation of a Physics-based Prognostic Model with Incomplete Data A Rail Wear Case Study
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Mar 11, 2023
Annemieke Meghoe
Richard Loendersloot
Tiedo Tinga
Abstract
While the development of prognostic models is nowadays rather feasible, the implementation and validation thereof can still create many challenges. One of the main challenges is the lack of high-quality input data like operational data, environmental data, maintenance data and the limited amount of degradation or failure data. The uncertainty in the output of the prognostic model needs to be quantified before it can be utilised for either model validation or actual maintenance decision support. This study, therefore, proposes a generic framework for prognostic model validation with limited data based on uncertainty propagation. This is realised by using sensitivity indices, correlation coefficients, Monte Carlo simulations and analytical approaches. For demonstration purposes, a rail wear prognostic model is used. The demonstration concludes that by following the generic framework, the prognostic model can be validated, and as a result, realistic maintenance advice can be given to rail infrastructure managers, even when limited data is available.
##plugins.themes.bootstrap3.article.details##
Keywords
physics based, rail wear, prognostics, maintenance
References
Archard, J. F. (1953). Contact and rubbing of flat surfaces. Journal of Applied Physics, 24(8), 981-988. doi: 10.1063/1.1721448
Atamuradov, V., Medjaher, K., & Noureddine, Z. (2017). Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 60, 1-32.
Barchiesi, D., Kessentini, S., & Grosges, T. (2011). Uncertainty analysis of nanoparticles for cancer photothermal therapy. In Advances in safety, reliability and risk management: Proceedings of the european safety and reliability conference (esrel 2011) (p. 2197–2204).
Chen, W., Baghdasaryan, L., Buranathiti, T., & Cao, J. (2004). Model validation via uncertainty propagation and data transformations. AIAA Journal, 42(7), 1406-1415. doi: 10.2514/1.491
Chen, W., Jin, R., & Sudjianto, A. (2005). Analytical variance-based global sensitivity analysis in simulation-based design under uncertainty. Journal of Mechanical Design, 127(5), 875. doi: 10.1115/1.1904642
Chollet, H. (2017). Estimation of the distribution of the wheel-rail friction coefficient in curves during the dynotrain project [Conference Proceedings]. In C. C. T. M. Maksym Spiryagin Timothy Gordon (Ed.), 25th international symposium on dynamics of vehicles on roads and tracks. doi: https://doi.org/10.1201/9781351057189
Corbetta, M., Kulkarni, C., Banerjee, P., & Robinson, E. (2021). Uncertainty quantification framework for autonomous system tracking and health monitoring. International Journal of Prognostics and Health Management, 12(3). doi: 10.36001/ijphm.2021.v12i3.2936
Dirks, B. (2015). Simulation and measurement of wheel on rail fatigue and wear (Unpublished doctoral dissertation). KTH Royal Institute of Technology.
Enblom, R. (2009). Deterioration mechanisms in the wheel rail interface with focus on wear prediction: a literature review. Vehicle System Dynamics, 47(6), 661-700. doi: 10.1080/00423110802331559
Gao, L., & Zhang, Z. (2008). Robust optimization for managing pavement maintenance and rehabilitation. Transportation Research Record, 2084(1), 55-61. doi: 10.3141/2084-07
Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724-739. doi: j.ymssp.2008.06.009
Hills, R. G., & Trucano, T. G. (1999). Statistical validation of engineering and scientific models: background (Tech. Rep. No. SAND99-1256). Sandia National Laboratories.
Ignesti, M., Innocenti, A., Marini, L., Meli, E., & Rindi, A. (2014). Development of a model for the simultaneous analysis of wheel and rail wear in railway systems. Multibody System Dynamics, 31(2), 191-240. doi: 10.1007/s11044-013-9360-0
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. doi: j.ymssp.2005.09.012
Jendel, T. (2002). Prediction of wheel profile wear-comparisons with field measurements. Wear, 253(1- 2), 89-99. doi: Pii S0043-1648(02)00087-X Doi 10.1016/S0043-1648(02)00087-X
Kennedy, M. C., & O’Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3), 425-464. doi: 10.1111/1467-9868.00294
Meghoe, A., Loendersloot, R., Bosman, R., & Tinga, T. (2018). Rail wear estimation for predictive maintenance: a strategic approach. In European conference of the prognostics and health management society 2018.
Meghoe, A., Loendersloot, R., & Tinga, T. (2019). Rail wear and remaining life prediction using meta-models. International Journal of Rail Transportation, 1-26. doi: 10.1080/23248378.2019.1621780
Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71.
Nejadseyfi, O., Geijselaers, H. J. M., & van den Boogaard, A. H. (2018). Evaluation and assessment of non-normal output during robust optimization. Structural and Multidisciplinary Optimization, 59(6), 2063-2076. doi: 10.1007/s00158-018-2173-2
Pannell, D. (1997). Sensitivity analysis of normative economic models: Theoretical framework and practical strategies. Agricultural Economics, 16, 139-152.
Parkinson, A., Sorensen, C., & Pourhassan, N. (1993). A general approach for robust optimal design. Journal of Mechanical Design, 115(1), 74-80. doi: 10.1115/1.2919328
Pearce, T. G., & Sherratt, N. D. (1991). Prediction of wheel profile wear. Wear, 144(1-2), 343-351. doi: 10.1016/0043-1648(91)90025-P
Popovici, R. (2010). Friction in wheel - rail contacts (Unpublished doctoral dissertation). University of Twente.
Popovic, Z., Lazarevic, L., Brajovic, L., & Vilotijevic, M. (2015). The importance of rail inspections in the urban area -aspect of head checking rail defects. Procedia Engineering, 117, 596-608. doi: 10.1016/j.proeng.2015.08.220
Ramalho, A. (2015). Wear modelling in rail-wheel contact. Wear, 330, 524-532. doi: 10.1016/j.wear.2015.01.067
Rong, K.-j., Xiao, Y.-l., Shen, M.-x., Zhao, H.-p., Wang, W.-J., & Xiong, G.-y. (2021). Influence of ambient humidity on the adhesion and damage behavior of wheel–rail interface under hot weather condition. Wear, 486-487, 204091. doi: https://doi.org/10.1016/j.wear.2021.204091
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52-53, 228-247. doi: j.ymssp.2014.05.029
Sheinman, E. (2012). Wear of rails. a review of the american press. Journal of Friction and Wear, 33(4), 308-314. doi: 10.3103/S1068366612040101
Soleimani, H., &Moavenian,M. (2017). Tribological aspects of wheel-rail contact: A review of wear mechanisms and effective factors on rolling contact fatigue. Urban Rail Transit, 3(4), 227-237. doi: 10.1007/s40864-017-0072-2
Tamssaouet, F., Nguyen, K. T. P., Medjaher, K., & Orchard, M. (2021). Fresh new look for system-level prognostics. , 12(2). doi: 10.36001/ijphm.2021.v12i2.2777
Trummer, G., Lee, Z. S., Lewis, R., & Six, K. (2021). Modelling of frictional conditions in the wheel–rail interface due to application of top-of-rail products. Lubricants, 9(10), 100. doi: 10.3390/lubricants9100100
Uusitalo, L., Lehikoinen, A., Helle, I., & Myrberg, K. (2015). An overview of methods to evaluate uncertainty of deterministic models in decision support. Environmental Modelling & Software, 63, 24-31. doi: 10.1016/j.envsoft.2014.09.017
Wu, Y. T., Millwater, H. R., & Cruse, T. A. (1990). Advanced probabilistic structural analysis method for implicit performance functions. AIAA Journal, 28(9), 1663-1669. doi: 10.2514/3.25266
Zhang, Y., Xiong, R., He, H., & Pecht, M. (2019). Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries. Journal of Cleaner Production, 212, 240-249. doi: https://doi.org/10.1016/j.jclepro.2018.12.041
Zobory, I. (1997). Prediction of wheel/rail profile wear. Vehicle System Dynamics, 28(2-3), 221-259. doi: 10.1080/00423119708969355
Atamuradov, V., Medjaher, K., & Noureddine, Z. (2017). Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 60, 1-32.
Barchiesi, D., Kessentini, S., & Grosges, T. (2011). Uncertainty analysis of nanoparticles for cancer photothermal therapy. In Advances in safety, reliability and risk management: Proceedings of the european safety and reliability conference (esrel 2011) (p. 2197–2204).
Chen, W., Baghdasaryan, L., Buranathiti, T., & Cao, J. (2004). Model validation via uncertainty propagation and data transformations. AIAA Journal, 42(7), 1406-1415. doi: 10.2514/1.491
Chen, W., Jin, R., & Sudjianto, A. (2005). Analytical variance-based global sensitivity analysis in simulation-based design under uncertainty. Journal of Mechanical Design, 127(5), 875. doi: 10.1115/1.1904642
Chollet, H. (2017). Estimation of the distribution of the wheel-rail friction coefficient in curves during the dynotrain project [Conference Proceedings]. In C. C. T. M. Maksym Spiryagin Timothy Gordon (Ed.), 25th international symposium on dynamics of vehicles on roads and tracks. doi: https://doi.org/10.1201/9781351057189
Corbetta, M., Kulkarni, C., Banerjee, P., & Robinson, E. (2021). Uncertainty quantification framework for autonomous system tracking and health monitoring. International Journal of Prognostics and Health Management, 12(3). doi: 10.36001/ijphm.2021.v12i3.2936
Dirks, B. (2015). Simulation and measurement of wheel on rail fatigue and wear (Unpublished doctoral dissertation). KTH Royal Institute of Technology.
Enblom, R. (2009). Deterioration mechanisms in the wheel rail interface with focus on wear prediction: a literature review. Vehicle System Dynamics, 47(6), 661-700. doi: 10.1080/00423110802331559
Gao, L., & Zhang, Z. (2008). Robust optimization for managing pavement maintenance and rehabilitation. Transportation Research Record, 2084(1), 55-61. doi: 10.3141/2084-07
Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724-739. doi: j.ymssp.2008.06.009
Hills, R. G., & Trucano, T. G. (1999). Statistical validation of engineering and scientific models: background (Tech. Rep. No. SAND99-1256). Sandia National Laboratories.
Ignesti, M., Innocenti, A., Marini, L., Meli, E., & Rindi, A. (2014). Development of a model for the simultaneous analysis of wheel and rail wear in railway systems. Multibody System Dynamics, 31(2), 191-240. doi: 10.1007/s11044-013-9360-0
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. doi: j.ymssp.2005.09.012
Jendel, T. (2002). Prediction of wheel profile wear-comparisons with field measurements. Wear, 253(1- 2), 89-99. doi: Pii S0043-1648(02)00087-X Doi 10.1016/S0043-1648(02)00087-X
Kennedy, M. C., & O’Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3), 425-464. doi: 10.1111/1467-9868.00294
Meghoe, A., Loendersloot, R., Bosman, R., & Tinga, T. (2018). Rail wear estimation for predictive maintenance: a strategic approach. In European conference of the prognostics and health management society 2018.
Meghoe, A., Loendersloot, R., & Tinga, T. (2019). Rail wear and remaining life prediction using meta-models. International Journal of Rail Transportation, 1-26. doi: 10.1080/23248378.2019.1621780
Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71.
Nejadseyfi, O., Geijselaers, H. J. M., & van den Boogaard, A. H. (2018). Evaluation and assessment of non-normal output during robust optimization. Structural and Multidisciplinary Optimization, 59(6), 2063-2076. doi: 10.1007/s00158-018-2173-2
Pannell, D. (1997). Sensitivity analysis of normative economic models: Theoretical framework and practical strategies. Agricultural Economics, 16, 139-152.
Parkinson, A., Sorensen, C., & Pourhassan, N. (1993). A general approach for robust optimal design. Journal of Mechanical Design, 115(1), 74-80. doi: 10.1115/1.2919328
Pearce, T. G., & Sherratt, N. D. (1991). Prediction of wheel profile wear. Wear, 144(1-2), 343-351. doi: 10.1016/0043-1648(91)90025-P
Popovici, R. (2010). Friction in wheel - rail contacts (Unpublished doctoral dissertation). University of Twente.
Popovic, Z., Lazarevic, L., Brajovic, L., & Vilotijevic, M. (2015). The importance of rail inspections in the urban area -aspect of head checking rail defects. Procedia Engineering, 117, 596-608. doi: 10.1016/j.proeng.2015.08.220
Ramalho, A. (2015). Wear modelling in rail-wheel contact. Wear, 330, 524-532. doi: 10.1016/j.wear.2015.01.067
Rong, K.-j., Xiao, Y.-l., Shen, M.-x., Zhao, H.-p., Wang, W.-J., & Xiong, G.-y. (2021). Influence of ambient humidity on the adhesion and damage behavior of wheel–rail interface under hot weather condition. Wear, 486-487, 204091. doi: https://doi.org/10.1016/j.wear.2021.204091
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52-53, 228-247. doi: j.ymssp.2014.05.029
Sheinman, E. (2012). Wear of rails. a review of the american press. Journal of Friction and Wear, 33(4), 308-314. doi: 10.3103/S1068366612040101
Soleimani, H., &Moavenian,M. (2017). Tribological aspects of wheel-rail contact: A review of wear mechanisms and effective factors on rolling contact fatigue. Urban Rail Transit, 3(4), 227-237. doi: 10.1007/s40864-017-0072-2
Tamssaouet, F., Nguyen, K. T. P., Medjaher, K., & Orchard, M. (2021). Fresh new look for system-level prognostics. , 12(2). doi: 10.36001/ijphm.2021.v12i2.2777
Trummer, G., Lee, Z. S., Lewis, R., & Six, K. (2021). Modelling of frictional conditions in the wheel–rail interface due to application of top-of-rail products. Lubricants, 9(10), 100. doi: 10.3390/lubricants9100100
Uusitalo, L., Lehikoinen, A., Helle, I., & Myrberg, K. (2015). An overview of methods to evaluate uncertainty of deterministic models in decision support. Environmental Modelling & Software, 63, 24-31. doi: 10.1016/j.envsoft.2014.09.017
Wu, Y. T., Millwater, H. R., & Cruse, T. A. (1990). Advanced probabilistic structural analysis method for implicit performance functions. AIAA Journal, 28(9), 1663-1669. doi: 10.2514/3.25266
Zhang, Y., Xiong, R., He, H., & Pecht, M. (2019). Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries. Journal of Cleaner Production, 212, 240-249. doi: https://doi.org/10.1016/j.jclepro.2018.12.041
Zobory, I. (1997). Prediction of wheel/rail profile wear. Vehicle System Dynamics, 28(2-3), 221-259. doi: 10.1080/00423119708969355
Section
Technical Papers