PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
Tytuł artykułu

Strain-based running-reliability characterisation in time-domain for risk monitoring under various load conditions

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This aim of this paper is to characterise the strain-based fatigue life data in time-domain using the newly modelled running-reliability technique that considers the load sequence effect. Current established conventional strain life models do not consider dependence for fatigue life of low or high amplitudes, on which with occur first in the load history. Finite element analysis is carried out to ensure the strain signals are captured at the most critical region during road test at various conditions. Fatigue life of 2.74 × 104 to 6.07 × 105 cycle/block with mean cycle to failure of 4.32 × 106 to 7.00 × 106 cycle/block is predicted based on the cycle sequence effect using cycle-counting method. The newly modelled running-reliability technique is formulated to extract the features of high amplitude excitation obtained from the strain signals for characterising the fatigue reliability features under load sequence effect. Hence, the reliability-hazard relationship for fatigue reliability characterisation of strain-based approach in time-domain using running-reliability technique.
Rocznik
Strony
art. no. 186825
Opis fizyczny
Bibliogr. 53 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
autor
  • Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
autor
  • Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
autor
  • Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
autor
  • Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
Bibliografia
  • 1. Zhang N, Jiang G, Wu D, Chen H, Wu J. Fatigue reliability analysis of the brake pads considering strength degradation. Eksploatacja i Niezawodność – Maintenance and Reliability 2020; 22(4): 620-626. https://doi.org/10.17531/ein.2020.4.5.
  • 2. Li R, Yang Z, Chen G, Wu B. Analytical solutions for nonlinear deflections of corner-fillet leaf-springs. Mechanism and Machine Theory 2021; 157: 104182, https://doi.org/10.1016/j.mechmachtheory.2020.104182.
  • 3. Hryciów Z, Krasoń W, Wysocki J. The experimental tests on the friction coefficient between the leaves of the multi-leaf spring considering a condition of the friction surfaces. Eksploatacja i Niezawodność – Maintenance and Reliability 2018; 20(4): 682-688. https://doi.org/10.17531/ein.2018.4.19.
  • 4. Tran-Ngoc H, Khatir S, Le-Xuan T, Roeck G D, Bui-Tien T, Abdel-Wahab M. Finite element model updating of a multispan bridge with a hybrid metaheuristic search algorithm using experimental data from wireless triaxial sensors. Engineering with Computers 2022; 38(3): 1865–1883, https://doi.org/10.1007/s00366-021-01307-9.
  • 5. Vu-Huu T, Le-Thanh C, Nguyen-Xuan H, Abdel-Wahab M. Polygonal Finite Element for Two-Dimensional Lid-Driven Cavity Flow. Computers, Materials & Continua 2022; 70(3): 4217–4239, https://doi.org/10.32604/cmc.2022.020889.
  • 6. Ling Y, Ni J, Antonissen J, Hamouda H B, Voorde J V, Wahab M A. Numerical prediction of microstructure and hardness for low carbon steel wire Arc additive manufacturing components. Simulation Modelling Practice and Theory 2023; 122: 102664, https://doi.org/10.1016/j.simpat.2022.102664.
  • 7. Nguyen K D, Thanh C -L, Vogel F, Nguyen-Xuan H, Abdel-Wahab M. Crack propagation in quasi-brittle materials by fourth-order phase-field cohesive zone model. Theoretical and Applied Fracture Mechanics 2022; 118: 103236, https://doi.org/10.1016/j.tafmec.2021.103236.
  • 8. Tran V -T, Nguyen T -K, Nguyen-Xuan H, Abdel Wahab M. Vibration and buckling optimization of functionally graded porous microplates using BCMO-ANN algorithm. Thin-Walled Structures 2023; 182(Part B): 110267, https://doi.org/10.1016/j.tws.2022.110267.
  • 9. Pastorcic D, Vukelic G, Bozic Z. Coil spring failure and fatigue analysis. Eng Fail Anal 2019; 99: 310-318, https://doi.org/10.1016/j.engfailanal.2019.02.017.
  • 10. Giannakis E, Savaidis G. Local stress based fatigue assessment of multiaxially stressed automotive antiroll bars. Eng Fail Anal 2021; 126: 105472, https://doi.org/10.1016/j.engfailanal.2021.105472.
  • 11. Thillikkani S, Nataraj M, King M F L. Failure analysis of Shackle Bracket in Airbus suspension under dynamic loading conditions. Eng Fail Anal 2021; 120: 105087, https://doi.org/10.1016/j.engfailanal.2. 020.105087.
  • 12. Bakir M, Ozmen B, Donertas C. Correlation of Simulation, Test Bench and Rough Road Testing in terms of Strength and Fatigue Life of a Leaf Spring. Procedia Eng 2018; 213: 303-312, https://doi.org/10.1016/j.proeng.2018.02.031.
  • 13. Li H -W -X, Chelidze D. Fatigue life estimation of structures under statistically and spectrally similar variable amplitude loading. Mech Syst Signal Proc 2021; 161: 107856, https://doi.org/10.1016/j.ymssp.2021.107856.
  • 14. Zhang L, Jiang B, Zhang P, Yan H, Xu X, Liu R, Tang J, Ren C. Methods for fatigue-life estimation: A review of the current status and future trends. Nanotechnol. Precis. Eng. 2023; 6(2): 025001, https://doi.org/10.1063/10.0017255.
  • 15. Abdullah L, Singh S S K, Abdullah S, Azman A. H, Ariffin A K, Kong Y S. The needs of power spectral density in fatigue life prediction of heavy vehicle leaf spring, Journal of Mechanical Science and Technology 2020; 34(6): 2341-2346, http://doi.org/10.1007/s12206-020-0510-z.
  • 16. VanDerHorn E, Wang Z, Mahadevan S. Towards a digital twin approach for vessel-specific fatigue damage monitoring and prognosis. Reliab Eng Syst Saf 2022; 219: 108222, https://doi.org/10.1016/j.ress.2021.108222.
  • 17. Szmytka F, Charkaluk E, Constantinescu A, Osmond P. Probabilistic Low Cycle Fatigue criterion for nodular cast-irons. Int J Fatigue 2020; 139: 105701, https://doi.org/10.1016/j.ijfatigue.2020.105701.
  • 18. Gong C, Frangopol D M. Time-variant hull girder reliability considering spatial dependence of corrosion growth, geometric and material properties. Reliab Eng Syst Saf 2020; 193: 106612, https://doi.org/10.1016/j.ress.2019.106612.
  • 19. Long X Y, Liu K, Jiang C, Xiao Y, Wu S C. Uncertainty propagation method for probabilistic fatigue crack growth life prediction. Theoretical and Applied Fracture Mechanics 2019; 103: 102268, https://doi.org/10.1016/j.tafmec.2019.102268.
  • 20. Fernández-Canteli A, Castillo E, Blasón S. A methodology for phenomenological analysis of cumulative damage processes. Application to fatigue and fracture phenomena. Int J Fatigue 2021; 150: 106311, https://doi.org/10.1016/j.ijfatigue.2021.106311.
  • 21. Muth A, John R, Pilchak A, Kalidindi S R, McDowell D L. Analysis of Fatigue Indicator Parameters for Ti-6Al-4V microstructures using extreme value statistics in the HCF regime. Int J Fatigue 2021; 145: 106096, https://doi.org/10.1016/j.ijfatigue.2020.106096.
  • 22. Mendler A, Döhler M, Ventura C E. A reliability-based approach to determine the minimum detectable damage for statistical damage detection. Mech Syst Signal Proc 2021; 154: 107561, https://doi.org/10.1016/j.ymssp.2020.107561.
  • 23. Braga J A P, Andrade A R. Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component. Reliab Eng Syst Saf 2021; 216: 107932, https://doi.org/10.1016/j.ress.2021.107932.
  • 24. Zheng G, Liao Y, Chen B, Zhao S, Wei H. Multi-axial load spectrum extrapolation method for fatigue durability of special vehicles based on extreme value theory. Int J Fatigue 2024; 178; 108014, https://doi.org/10.1016/j.ijfatigue.2023.108014.
  • 25. Wang J, You S, Wu Y, Zhang Y, Bin S. A Method of Selecting the Block Size of BMM for Estimating Extreme Loads in Engineering Vehicles. Mathematical Problems in Engineering 2016; 2016; 6372197, https://doi.org/10.1155/2016/6372197.
  • 26. Putra T E, Husaini, Ikbal M, Automotive suspension component behaviors driven on flat and rough road surfaces, Heliyon 2021; 7(7): e07528, https://doi.org/10.1016/j.heliyon.2021.e07528.
  • 27. Haiba M, Barton D C, Brooks P C, Levesley M C. The development of an optimisation algorithm based on fatigue life. Int J Fatigue 2003; 25(4): 299-310, https://doi.org/10.1016/S0142-1123(02)00143-3.
  • 28. Konvicny D, Makys P, Furmanik M. Effect of increasing the sampling frequency with respect to the bandwidth of the PI controller of current control loop. Transportation Research Procedia 2021; 55: 935-940, https://doi.org/10.1016/j.trpro.2021.07.190.
  • 29. Kong Y S, Abdullah S, Haris S M, Omar M Z, Schramm D. Generation of artificial road profile for automobile spring durability analysis. Jurnal Kejuruteraan 2018; 30(2): 123-128, http://dx.doi.org/10.17576/jkukm-2018-30(2).
  • 30. Rashid A A A, Poi A, Jawi Z, Kassim K A. Revisiting Speed Management Strategies in Malaysia. Journal of the Society of Automotive Engineers Malaysia 2021; 5(2): 318–330, https://doi.org/10.56381/jsaem.v5i2.175.
  • 31. Marques J M E, Benasciutti D. Variance of the fatigue damage in non-Gaussian stochastic processes with narrow-band power spectrum. Structural Safety 2021; 93: 102131, https://doi.org/10.1016/j.strusafe.2021.102131.
  • 32. 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 Niezawodność – Maintenance and Reliability 2022; 24(1): 62-69, https://doi.org/10.17531/ein.2022.1.8.
  • 33. Ren S, Chen H, Zheng R. Modified time domain randomization technique for multi-shaker non-stationary non-Gaussian random vibration control. Mech Syst Sig Proc 2024; 213: 111311, https://doi.org/10.1016/j.ymssp.2024.111311.
  • 34. Sofi A, Giunta F, Muscolino G. Fatigue life bounds for randomly excited structures with interval parameters via sensitivity analysis. Prob Eng Mech 2022; 69: 103307, https://doi.org/10.1016/j.probengmech.2022.103307.
  • 35. Fan W, Li Z, Yang X. A spectral method for fatigue analysis based on nonlinear damage model. Int J Fatigue 2024; 182: 108188, https://doi.org/10.1016/j.ijfatigue.2024.108188.
  • 36. Mysior M, Pietrucha G, Koziołek S. Strength testing of a modular trailer with a sandwich platform. Eksploatacja i Niezawodność – Maintenance and Reliability 2022; 24(1): 163-169, https://doi.org/10.17531/ein.2022.1.18.
  • 37. Pham Q H, Antoni J, Tahan A, Gagnon M, Monette C. Simulation of non-Gaussian stochastic processes with prescribed rainflow cycle count using short-time Fourier transform. Prob Eng Mech 2022; 68: 103220, https://doi.org/10.1016/j.probengmech.2022.103220.
  • 38. Cazin D, Braut S, Božić Ž, Žigulić R. Low cycle fatigue life prediction of the demining tiller tool. Eng Fail Anal 2020; 111: 104457, https://doi.org/10.1016/j.engfailanal.2020.104457.
  • 39. Zhu S -P, Lei Q, Huang H -Z, Yang Y -J, Peng W. Mean stress effect correction in strain energy-based fatigue life prediction of metals, Int J Damage Mech 2017; 26(8): 1219, https://doi.org/10.1177/1056789516651920.
  • 40. El-Zeghayar M, Topper T, Bonnen J J. Derivation of Effective Strain-Life Data, Crack Closure Parameters and Effective Crack Growth Data from Smooth Specimen Fatigue Tests SAE Int J Mater Manf 2013; 6: 576–588, https://doi.org/10.4271/2013-01-1779.
  • 41. Xia F -L, Zhu S -P, Liao D, Dantas R, Correia J A F O, De Jesus A M P. Isodamage curve-based fatigue damage accumulation model considering the exhaustion of static toughness. Eng Fail Anal 2020; 115: 104575, https://doi.org/10.1016/j.engfailanal.2020.104575.
  • 42. Kadhim N A, Abdullah S, Ariffin A K. Effective strain damage model associated with finite element modelling and experimental validation. Int J Fatigue 2012; 36: 194–205, https://doi.org/10.1016/j.ijfatigue.2011.07.012.
  • 43. Nagode M, Oman S, Klemenc J, Panić B. Gumbel mixture modelling for multiple failure data. Reliab Eng Syst Saf 2023; 230: 108946, https://doi.org/10.1016/j.ress.2022.108946.
  • 44. Xiang G, Bacharoudis K C, Vassilopoulos A P. Probabilistic fatigue model for composites based on the statistical characteristics of the cycles to failure. Int J Fatigue 2022; 163; 107085, https://doi.org/10.1016/j.ijfatigue.2022.107085.
  • 45. Pourdavood M, Bocher P. Statistical modeling of microstructurally short crack growth in high cycle fatigue. Materials Science & Engineering A 2024; 146092, doi: https:// doi.org/10.1016/j.msea.2024.146092.
  • 46. Wang Y, Xia A, Qin G. Probabilistic modeling for reliability analysis of buried pipelines subjected to spatiotemporal earthquakes. Prob Eng Mech 2022; 69: 103315, https://doi.org/10.1016/j.probengmech.2022.103315.
  • 47. Selech J, Andrzejczak K. An aggregate criterion for selecting a distribution for times to failure of components of rail vehicles. Eksploatacja i Niezawodność – Maintenance and Reliability 2020; 22(1): 102-111. https://doi.org/10.17531/ein.2020.1.12.
  • 48. Li X, Li S, Li J, Su Y. Nonstationary time-varying extreme value of downburst-induced wind loads based on transformed stationary method. Prob Eng Mech 2022; 70: 103345, https://doi.org/10.1016/j.probengmech.2022.103345.
  • 49. Wang C, Liu Y, Wang D, Wang G, Wang D, Yu C. Reliability evaluation method based on dynamic fault diagnosis results: A case study of a seabed mud lifting system. Reliab Eng Syst Saf 2021; 214: 107763, https://doi.org/10.1016/j.ress.2021.107763.
  • 50. Venturini S, Rosso C, Velardocchia M. An automotive steel wheel digital twin for failure identification under accelerated fatigue tests. Eng Fail Anal 2024; 158: 107979, https://doi.org/10.1016/j.engfailanal.2024.107979.
  • 51. Żyluk A, Zieja M, Grzesik N, Tomaszewska J, Kozłowski G, Jasztal M. Implementation of the Mean Time to Failure Indicator in the Control of the Logistical Support of the Operation Process. Appl Sci 2023; 13(7): 4608, https://doi.org/10.3390/app13074608.
  • 52. Schumacher J, Clausen B. Calculation of the Fatigue Limit of High-Strength Steel Specimens at Different Loading Conditions Based on Inclusion Sizes. Steel Research Int 2021; 92: 2100252, https://doi.org/10.1002/srin.202100252.
  • 53. Li H, Deng Z -M, Golilarz N A, Soares C G. Reliability analysis of the main drive system of a CNC machine tool including early failures. Reliab Eng Syst Saf 2021; 215: 107846, https://doi.org/10.1016/j.ress.2021.107846.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-05206876-7ec7-4ba8-848b-eda17f2ca461
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.