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http://dx.doi.org/10.9765/KSCOE.2020.32.3.147

Estimation of Residual Useful Life and Tracking of Real-time Damage Paths of Rubble-Mound Breakwaters Using Stochastic Wiener Process  

Lee, Cheol-Eung (Department of Architectural, Civil, and Environmental Engineering, Kangwon National University)
Publication Information
Journal of Korean Society of Coastal and Ocean Engineers / v.32, no.3, 2020 , pp. 147-160 More about this Journal
Abstract
A stochastic probabilistic model for harbor structures such as rubble-mound breakwater has been formulated by using the generalized Wiener process considering the nonlinearity of damage drift and its nonlinear uncertainty, by which the damage path with real-time can be tracked, the residual useful lifetime at some age can also be analyzed properly. The formulated stochastic model can easily calculate the probability of failure with the passage of time through the probability density function of cumulative damage. In particular, the probability density functions of residual useful lifetime of the existing harbor structures can be derived, which can take into account the current age, its present damage state and the future damage process to be occurred. By using the maximum likelihood method and the least square method together, the involved parameters in the stochastic model can be estimated. In the calibration of the stochastic model presented in this paper, the present results are very well similar with the results of MCS about tracking of the damage paths as well as evaluating of the density functions of the cumulative damage and the residual useful lifetime. MTTF and MRL are also evaluated exactly. Meanwhile, the stochastic probabilistic model has been applied to the rubble-mound breakwater. The related parameters can be estimated by using the experimental data of the cumulative damages of armor units measured as a function of time. The theoretical results about the probability density function of cumulative damage and the probability of failure are very well agreed with MCS results such that the density functions of the cumulative damage tend to move to rightward and the amounts of its uncertainty are increased as the elapsed time goes on. Thus, the probabilities of failure with the elapsed time are also increased sharply. Finally, the behaviors of residual useful lifetime have been investigated with the elapsed age. It is concluded for rubble-mound breakwaters that the probability density functions of residual useful lifetime tends to have a longer tail in the right side rather than the left side because of the gradual increases of cumulative damage of armor units. Therefore, its MRLs are sharply decreased after some age. In this paper, the special attentions are paid to the relationship of MTTF and MRL and the elapsed age of the existing structure. In spite of that the sum of the elapsed age and MRL must be equal to MTTF deterministically, the large difference has been shown as the elapsed age is increased which is due to the uncertainty of cumulative damage to be occurred in the future.
Keywords
residual useful lifetime; wiener process; maximum likelihood method; damage path; rubble-mound breakwater;
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1 Ahmad, R. and Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Compu. Ind. Eng., 63(1), 135-149.   DOI
2 Bagdonavicius, V. and Nikulin, M. (2000). Estimation in degradation models with explanatory variables. Lifetime Data Analysis., 7, 85-103.   DOI
3 CEM (2006). Coastal Engineering Manual, U.S. Army Corps of Engineers, Washington D.C., USA.
4 Chhikara, R.S. and Folks, J.L. (1977). The inverse Gaussian distribution as a lifetime model. Technometrics, 19, 461-468.   DOI
5 Cox, D.R. and Miller, H.D. (1965). The theory stochastic processes, Methuen and Com., London.
6 Crowder, M. and Lawless, J. (2007). On a scheme for predictive maintenance. European J. of Oper. Res., 176, 1713-1722.   DOI
7 Cui, L.R., Loh, H.T. and Xie, M. (2004). Sequential inspection strategy for multiple systems under availability requirement. European J. of Oper. Res., 155, 170-177.   DOI
8 Dieulle, L., Berengguer, C., Grall, A. and Roussignol, M. (2003). Sequential condition-based maintenance scheduling for a deteriorating system. European J. of Oper. Res., 150, 451-461.   DOI
9 Doksum, K.A. and Hoyland, A. (1992). Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse Gaussian distribution. Technometrics, 34, 74-82.   DOI
10 Ebrahimi, N. (2009). The mean function of a repairable system that is subjected to an imperfect repair policy. IIE Transactions, 41, 57-64.   DOI
11 Elwang, A. and Gebraeel, N.Z. (2009). Real-time estimation of mean remaining life using sensor-based degradation models. J. Manuf. Sci. Eng., 131, 51-59.
12 Gebraeel, N.Z., Lawley, M.A., Li, R. and Ryan, J.K. (2005). Residual-life distribution from component degradation signals: A bayesian approach. IIE Transactions, 37, 543-557.   DOI
13 Jin, G., Matthews, D.E. and Zhou, Z. (2013). A bayesian framework for on-line degradation assessement and residual life prediction of secondary batteries in spacecraft. Reliability Engineering and System Safety, 113, 7-20.   DOI
14 Gasperin, M., Juricic, D, Baskoski, P. and Jozef, V. (2011). Modelbased prognostics of gear health using stochastic dynamic models. Mechanical Systems and Signal Processing, 25, 537-538.   DOI
15 Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P. and Sun, Y. (2009). A review on degradation models in reliability analysis, Proceeding of the 4th World Congress on Engineering Asset Management, Athens, Greece.
16 Guo, C., Wang, W., Guo, B. and Si, A. (2013). A maintenance optimization model for mission-oriented systems based on Wiener degradation. Reliability Engineering and System Safety, 111, 183-194.   DOI
17 Kuniewslci, S.P., van der Weide, J.A.M. and van Noortwijk, J.M. (2009). Sampling inspection for the evaluation of time-dependent reliability of deteriorating systems under imperfect defect detection. Reliability Engineering and System Safety, 94, 1480-1490.   DOI
18 Lee, C.-E. (2015). Estimation of time-dependent damage paths of armors of rubble-mound breakwaters using stochastic processes. J. of Korean Society of Coastal and Ocean Engineers, 27(4), 246-257 (in Korean).   DOI
19 Lee, C.-E. (2016). Condition-based model for preventive maintenance of armor units of rubble-mound breakwaters using stochastic process. J. of Korean Society of Coastal and Ocean Engineers, 28(4), 191-201 (in Korean).   DOI
20 Lee, C.-E. and Park, D.H. (2017). Discounted cost model of condition-based maintenance regarding cumulative damage of armor units of rubble-mound breakwaters as a discrete-time stochastic processes. J. of Korean Society of Coastal and Ocean Engineers, 29(2), 109-120 (in Korean).   DOI
21 Li, N., Lei, Y., Yan, T., Li, N. and Han, T. (2019). A Wiener-process-model-based method for remaining useful life prediction considering unit-to-unit variability. IEEE Transactions on Indus. Elec., 66, 2092-2101.   DOI
22 Lee, C.-E. (2019). Prediction of expected residual useful life of rubble-mound breakwaters using stochastic gamma process. J. of Korean Society of Coastal and Ocean Engineers, 31(3), 158-169 (in Korean).   DOI
23 Le Son, K., Fouladirad, M., Barros, A., Levrat, E. and Lung, B. (2013). Remaining useful life estimation based on stochastic deterioration models: A comparative study. Reliability Engineering and System Safety, 112, 165-175.   DOI
24 Li, N., Lei, Y., Guo, L., Yan, T. and Lin, J. (2017). Remaining useful life prediction based on a general expression of stochastic process models. IEEE Transactions on Indus. Elec., 64, 5709-5718.   DOI
25 Melby, J.A. (1999). Damage progression on breakwaters, Ph.D. Thesis, Dept. of Civil Eng., U. of Delaware, USA.
26 Moler, C. (2004). Numerical computing with MATLAB, Society for Industrial Mathematics.
27 Nicolai, R.P., Frenk, B.G. and Dekker, R. (2009). Modelling and optimizing imperfect maintenance of coatings on steel structures. Structural Safety, 37, 234-244.   DOI
28 Pandey, M.D., Yuan, X.-X. and van Noortwijk, J.M. (2009). The influence of temporal uncertainty of deterioration on life-cycle management of structures. Structure and Infrastructure Engineering, 5(2), 145-156.   DOI
29 Pecht, M. and Jaai, R. (2010). A prognostic and health management roadmap for information and electronic-rich system. Microelectronics Reliability, 50, 317-323.   DOI
30 Peng, C.Y. and Tseng, S.T. (2009). Mis-specification analysis of linear degradation models. IEEE Transactions on Reliability, 58, 44-455.
31 Si, X.-S., Wang, W., Hu, C.H. and Zhou, D.H. (2011). Remaining useful lifetime estimation - A review on the statistical data driven approaches. European J. of Oper. Res., 213, 1-14.   DOI
32 PIANC (1992). Analysis of rubble mound breakwaters, Supplement to Bull. No. 78/79, Brussels, Belgium.
33 Ross, S. (1996). Stochastic processes, Wiley Series in Probability and Statistics, John Wiley & Sons, Inc.
34 Shahraki, A.F., Yaadav, O.P. and Liao, H. (2017). A review on degradation modelling and its engineering applications. Int. J. of Performability Eng., 13(3) 299-314.
35 Si, X.S., Wang, W., Hu, C.H. and Zhou, D.H. (2012). Remaining useful life estimation based on nonlinear diffusion degradation process. IEEE Transactions on Reliability, 61, 50-67.   DOI
36 Singpurwalla, N.D. (1995). Survival in dynamic environments. Statistical Science, 10(1), 86-103.   DOI
37 Tsai, C.C., Tseng, S.T. and Balakrishnam, N. (2011). Mis-specification analyses of gamma and Wiener degradation processes. J. Stat. Plan Inference, 141, 3725-3735.   DOI
38 Tseng, S.T., Tang, J. and Ku, L.H. (2003). Determination of optimal burn-in parameters and residual life for highly reliable products. Naval Re. Logisics, 50, 1-14.   DOI
39 Tseng, S. and Peng, C. (2007). Stochastic diffusion modelling of degradation data. J. Data Sci., 5, 315-333.   DOI
40 van Noortwijk, J.M. (2009). A survey of the application of gamma processes in maintenance. Reliability Engineering and System Safety, 94, 2-21.   DOI
41 Wang, W., Scarf, P.A. and Smith, M.A.J. (2000). On the application of a model of condition-based maintenance. European J. of Oper. Res., 51, 1218-1227.
42 van Noortwijk, J.M., van der Weide, J.A.M., Kallena, M.J. and Pandey, M.D. (2007). Gamma processes and peak-over-threshold distributions for time-dependent reliability. Reliability Engineering and System Safety, 92, 1651-1658.   DOI
43 Wang, W. (2007). A two-stage prognosis model in condition based maintenance. European J. of Oper. Res., 182, 1177-1187.   DOI
44 Wang, X. (2010). Wiener processes with random effects for degradation data. J. Multivar. Anal., 101, 340-351.   DOI
45 Wang, W.B., Carr, M., Xu, W.J. and Kobbacy, A.K. (2010). A model for residual life prediction based on brownian motion with an adaptive drift. Microelectron Reliab., 51, 285-293.   DOI
46 Wang, X., Jiang, P., Guo, B. and Cheng, Z. (2013). Real-time reliability evaluation with a general Wiener process-based degradation model. Quality and Reliability Eng. Int., 30, 205-220.   DOI
47 Wang, X., Balakrishnan, N. and Guo, B. (2014). Residual life estimation based on a generalized Wiener degradation process. Reliability Engineering and System Safety, 124, 13-23.   DOI
48 Whitmore, G.A. (1995). Estimating degradation by a Wiener diffusion process subject to measurement error. Lifetime Data Analysis, 1, 307-319.   DOI
49 Whitmore, G.A. and Schenkelberg, F. (1997). Modelling accelerated degradation data using Wiener diffusion with a time scale transformation. Lifetime Data Analysis, 3, 27-45.   DOI
50 Ye, Z., Shen, Y. and Xie, M. (2012). Degradation-based burn-in with preventive maintenance. European J. of Oper. Res., 221, 360-367.   DOI