Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
A remaining useful life (RUL) prediction model based on the nonlinear Wiener process is proposed to better tackle the life evaluation problem in the slope degradation process. Taking the displacement of the slope as its performance degradation index, and the nonlinear Wiener process is used to establish the RUL prediction model of the slope. For this model, the least squares method (LSM) is used to estimate the drift coefficients, the maximum likelihood estimation method (MLEM) is used to estimate the diffusion parameters, and then the probability density function (PDF) of the RUL of the slope is deduced and the RUL is predicted. The proposed model is verified by slope engineering examples. The results demonstrated that the RUL of the degradation model based on the nonlinear Wiener process has a greater prediction accuracy than the linear Wiener process. Because the various nonlinear functions have varying slope adaptations, and it can predict the RUL of a slope more accurately, which can provide more reliable preventive maintenance decisions.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
art. no. 187160
Opis fizyczny
Bibliogr. 40 poz., rys., tab., wykr.
Twórcy
autor
- School of Civil and Hydraulic Engineering, Hefei University of Technology, China
- Anhui Province Key Laboratory of Water Conservancy and Water Resources, China
autor
- School of Civil and Hydraulic Engineering, Hefei University of Technology, China
autor
- School of Civil and Hydraulic Engineering, Hefei University of Technology, China
- Anhui Province Key Laboratory of Water Conservancy and Water Resources, China
Bibliografia
- 1. Basharat M, Riaz M T, Jan M Q, Xu C, Riaz S. A review of landslides related to the 2005 Kashmir Earthquake: implication and future challenges. Natural Hazards 2021; 108: 1-30. DOI: 10.1007/s11069-021-04688-8
- 2. Huang R Q. Large landslides and their occurrence mechanisms in China since the 20th century. Journal of Rock Mechanics and Geotechnical Engineering 2007; (03): 433-454. DOI:10.3321/j.issn:1000-6915.2007.03.001
- 3. Pardeshi S D, Autade S E, Pardeshi S S. Landslide hazard assessment: recent trends and techniques. SpringerPlus 2013; 2: 523. DOI: 10.1186/2193-1801-2-523
- 4. Osasan K S, Afeni T B. Review of surface mine slope monitoring techniques, Journal of Mining Science 2010; 46: 177-186. DOI: 10.1007/s10913-010-0023-8
- 5. Ren G L, Fu Y H, Li L Y. A review of research on monitoring and early warning of slope engineering disasters. Journal of Institute of Disaster-Prevention Science and Technology 2021; 23(1): 6-16. DOI: 10.3969/j.issn.1673-8047.2021.01.002
- 6. Mosallam A, Medjaher K, Zerhouni N. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing 2016; 27: 1037-1048. DOI: 10.1007/s10845-014-0933-4
- 7. Nayek P S, Gade M. Artificial neural network-based fully data-driven models for prediction of new mark sliding displacement of slopes. Neural Computing & Applications 2022; 34: 9191-9203. DOI: 10.1007/s00521-022-06945-8
- 8. Heng W, Guo Y, Xiong L, Liu W, Li G, Zhou X. Optical Fiber-Based Sensing, Measuring, and Implementation Methods for Slope Deformation Monitoring. IEEE Sensors Journal 2019; 19: 2786-2800. DOI: 10.1109/JSEN.2019.2891734
- 9. Xu Q, Tang M G, Xu K X, Huang X B. Research on the spatial and temporal evolution of landslides and early warning forecasting. Journal of Rock Mechanics and Engineering 2008; 27(6): 1104-1112. DOI: 10.3321/j.issn:1000-6915.2008.06.003
- 10. He K Q, Yang J B, Wang S J. Surface displacement vector angle of mounded slopes and its role and significance in stability prediction. Journal of Rock Mechanics and Engineering 2003; (12): 1976-1983. DOI: 10.3321/j.issn:1000-6915.2003.12.006
- 11. Ma WT. Prediction of slope displacements based on grey least squares support vector machin. Geotechnics 2010; 31(05): 1670-1674. DOI: 10.3969/j.issn.1000-7598.2010.05.055
- 12. Venkatesan M, Thangavelu A, Prabhavathy P. An Improved Bayesian Classification Data Mining Method for Early Warning Landslide Susceptibility Model Using GIS. Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing 2013; vol 202. Springer, India. DOI: 10.1007/978-81-322-1041-2_24
- 13. Cheng Y C, Jiang P, Tan G J, Sun Y Y, Jiao Y B. Real-time evaluation of soil slope stability based on displacement monitoring information. Journal of Jilin University (Engineering Edition) 2012; 42(06): 1487-1490. DOI: 10.13229/j. cnki.jdxbgxb2012.06.047
- 14. Feng G, Xia Y Y, Wang Z D, Yan M J. A dynamic early warning method for open pit slopes based on displacement information fusion. Chinese Journal of Safety Science 2022; 32(03): 116-122. DOI: 10.16265/j.cnki.issn1003-3033.2022.03.016
- 15. Chakraborty A, Goswami D. Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences 2017; 10, 385. DOI: 10.1007/s12517-017-3167-x
- 16. Qi Z F, Jiang Q H, Zhou C B, Xiang B Y, Shao J D. Inverse analysis method of slope displacement based on v-SVR and MVPSO algorithms and its application. Journal of Rock Mechanics and Engineering 2013; 32(06): 1185-1196. DOI: 10.3969/j.issn.1000-6915.2013.06.012
- 17. Liu Y, Long J, Li C, Zhan W. Physics-informed data assimilation model for displacement prediction of hydrodynamic pressure-driven landslide. Computers and Geotechnics 2024; 167: 106085. DOI: 10.1016/J.COMPGEO.2024.106085
- 18. Wang J B, Lei T J, Liu W K, Chen Y J, Yue J W, Liu B Y. Prediction analysis of landslide displacement trajectory based on the gradient descent method with multisource remote sensing observations. Geomatics, Natural Hazards and Risk 2023; 14(1): 143-175. DOI: 10.1080/19475705.2022.2158375
- 19. Lin Q Y, Yang Z P, Huang J, Deng J, Chen L, Zhang Y R. A landslide displacement prediction model based on the ICEEMDAN method and the TCN–Bi LSTM combined neural network. Water 2023; 15(24): 4247. DOI: 10.3390/W15244247
- 20. Kang J, Lu Y, Zhao B, Luo H, Meng J, Zhang Y. Remaining useful life prediction of cylinder liner based on nonlinear degradation model. Eksploatacja i Niezawodnosc-Maintenance and Reliability 2022; 24(1): 62-69. DOI:10.17531/ein.2022.1.8
- 21. Lyu Y, Jiang Y, Zhang Q, Chen C. Remaining useful life prediction with insufficient degradation data based on deep learning approach. Eksploatacja i Niezawodnosc- Maintenance and Reliability 2021; 23(4): 745-756. DOI: 10.17531/ein.2021.4.17
- 22. Rusin A, Tomala M. Steam turbine maintenance planning based on forecasting of life consumption processes and risk analysis. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2022; 24(3): 395-406. DOI: 10.17531/ein.2022.3.1
- 23. Wang X, Lin S, Wang S, He Z, Zhang C. Remaining Useful Life Prediction Based on the Wiener Process for an Aviation Axial Piston Pump. Chinese Journal of Aeronautics 2016; 29(3): 779-88. DOI: 10.1016/j.cja.2015.12.020
- 24. Freitas M A, de Toledo M L G, Colosimo E A, Pireset M C. Using degradation data to assess reliability: a case study on train wheel degradation. Quality & Reliability Engineering International 2010; 25(5): 607-629. DOI: 10.1002 /qre.995
- 25. Oliveira V R B, Colosimo E A. Comparison of Methods to Estimate the Time-to-failure Distribution in Degradation Tests. Quality & Reliability Engineering International 2004; 20(4): 363-373. DOI: 10.1002/qre.567
- 26. Zhu Y, Liu S W, Wei K X, Zuo H Y, Du R H, Shu Xiong. A novel based-performance degradation Wiener process model for real-time reliability evaluation of lithium-ion battery. Journal of Energy Storage, 50: 104313. DOI: 10.1016/j.est.2022.104313
- 27. Liu J Q, Xie J W, Zuo H F, Zhang M L. Residual life prediction of aero engines based on stochastic Wiener process. Journal of Aeronautics 2015; 36(2): 564-574. DOI: 10.7527/S1000-6893.2014.0312
- 28. Li M, Huang M. Research on the framework of reservoir bank PHM system and fault prediction technology based on safety monitoring. Water Science and Technology and Economy 2018; 24(06): 84-89. DOI: 10.3969/j.issn.1006-7175.2018.06.019
- 29. Feng X L, Huang M. Prediction of residual life of slopes of water diversion projects based on damage mechanics ofwiener process. Engineering and Construction 2020; 34(05): 923-926 (2020). DOI: 10.3969/j.issn.1673-5781.2020.05.045
- 30. Peng C Y, Tseng S T. Mis-specification Analysis of Linear Degradation Models. IEEE Transactions on Reliability 2009; 58(3): 444-455. DOI: 10.1109/TR.2009.2026784
- 31. Pei H, Hu C H, Si X S, Zhang Z X, Du D B. Remaining Life Prediction Information-based Maintenance Decision Model for Equipment Under Imperfect Maintenance. Journal of Automation 2018; 44(04): 719-729. DOI: 10.16383/j.aas.2017.c160534
- 32. Wang D, Tsui K L. Brownian motion with adaptive drift for remaining useful life prediction: revisited. Mechanical Systems and Signal Processing 2018; 99: 691-701. DOI: 10.1016/j.ymssp.20 17.07.015
- 33. Nan X K, Gao W K, Chen X F, Sun T Q, Ji H. Analysis of remaining service life of piston pumps based on nonlinear Wiener process. Hydraulics and Pneumatics 2020; (11): 45-52. DOI: 10.11832/j.issn.1000-4858.2020.11.008
- 34. Wang X L, Guo B, Cheng Z J, Jiang P. Residual life estimation based on bivariate Wiener degradation process with measurement errors. Journal of Central South University 2013; 20: 1844-1851. DOI: 10.1007/s11771-013-1682-9
- 35. Si X S, Wang W, Hu C H, Zhou D H, Pecht M G. Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process. IEEE Transactions on Reliability 2012; 61(1): 50-67. DOI: 10.1109/TR.2 011.2182221
- 36. Lin H, Cao P, Li J T, Liu Y K. Criteria for determining the critical instability state of slopes. Journal of Coal 2008; (06): 643-647. DOI: 10.3321/j.issn:0253-9993.2008.06.010
- 37. Miao H B, Yin K L, Li Y Y. Study on plane instability model and damage criterion of near-horizontal stratigraphic landslide. Hydrogeology and Engineering Geology 2009; 36(01): 69-74. DOI: 10.3969/j.issn.1000-3665.2009.01.016
- 38. Qian H T, Fang T, Lan J Y, Wang S J. Analysis of critical displacement for landslide stability in mountainous areas under strong earthquakes. Journal of Rock Mechanics and Engineering 2012; 31(S1): 2619-2628. DOI: 10.3969/j.issn.1000-6915.2012.z1.004
- 39. He K Q, Chen W G, Zhang P. Real-time monitoring of dynamic stability coefficients of creep-slip slopes and its displacement early warning criterion research. Journal of Rock Mechanics and Engineering 2016; 35(07): 1377-1385. DOI: 10.13722/j.cnki.jrme.2015.1535
- 40. DLT5353-2006. Design Specification for Slope of Hydropower and Water Conservancy Project. Northwest Survey and Design Institute of China Hydropower Consulting Group: Beijing, China, 2006.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-62636bfe-0bae-49fa-9caf-c650e5be4fac