Probabilistic fatigue life prediction using multi-layer perceptron with maximum entropy algorithm
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Abstract
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Research context
OpenAlex topics: Fatigue and fracture mechanics · Non-Destructive Testing Techniques · Structural Health Monitoring Techniques
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References (48)
Show source-provided references
1. A novel gear fatigue monitoring indicator and its application to remaining useful life prediction for spur gear in intelligent manufacturing systems10.1016/j.ijfatigue.2022.107459 ↗
2. Fracture mechanics for fatigue design of metallic components and small defect assessment10.1016/j.ijfatigue.2021.106550 ↗
3. Wöhler · 1870
4. The exponential law of endurance tests
5. New method for the statistical evaluation of constant stress amplitude fatigue-test results
6. A new function for fatigue curves characterization and its multiple merits10.1016/S0142-1123(00)00082-7 ↗
7. A maximum likelihood method for estimating P S N curves10.1016/S0142-1123(97)00037-6 ↗
8. Vibration-based updating of wear prediction for spur gears10.1016/j.wear.2019.01.017 ↗
9. A review of vibration-based gear wear monitoring and prediction techniques10.1016/j.ymssp.2022.109605 ↗
10. A probabilistic method for predicting fatigue life controlled by defects10.1016/S0921-5093(98)00772-2 ↗
11. A novel method for structure’s fatigue life scatter simulation under material variability10.1016/j.ijfatigue.2021.106296 ↗
12. Die Levbensdauer von kugellagern
13. Parameters estimation and fatigue life prediction of sisal fibre reinforced foam concrete10.1016/j.jmrt.2022.07.096 ↗
14. Reliability fatigue life and a new S-N curve model of composite laminates under tensile-tensile fatigue load10.1007/s10443-020-09847-x ↗
15. A probabilistic Stüssi function for modelling the SN curves and its application on specimens made of steel S355J2+ N10.1016/j.ijfatigue.2018.07.041 ↗
16. Life prediction for rate-dependent low-cycle fatigue of PA6 polymer considering ratchetting: Semi-empirical model and neural network based approach10.1016/j.ijfatigue.2020.105619 ↗
17. High cycle fatigue SN curve prediction of steels based on transfer learning guided long short term memory network10.1016/j.ijfatigue.2022.107050 ↗
18. An improved neural computing method for describing the scatter of S–N curves10.1016/j.ijfatigue.2007.01.018 ↗
19. A neural-network approach to describe the scatter of cyclic stress-strain curves10.1016/j.matdes.2009.05.044 ↗
20. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network10.1016/j.ijfatigue.2022.107234 ↗
21. A combined reliability analysis approach with dimension reduction method and maximum entropy method10.1007/s00158-010-0546-2 ↗
22. Materials fatigue life distribution: A maximum entropy approach10.1520/JTE11970J ↗
23. Defect-induced fatigue scattering and assessment of additively manufactured 300M-AerMet100 steel: An investigation based on experiments and machine learning10.1016/j.engfracmech.2022.108352 ↗
24. Rate-dependent multiaxial life prediction for polyamide-6 considering ratchetting: Semi-empirical and physics-informed machine learning models10.1016/j.ijfatigue.2022.107086 ↗
25. A neural network approach to describing the scatter of S–N curves10.1016/j.ijfatigue.2005.08.002 ↗
26. Fault prediction of drag system using artificial neural network for prevention of dragline failure10.1016/j.engfailanal.2020.104542 ↗
27. Prediction of fatigue crack growth rate in welded tubular joints using neural network10.1016/j.ijfatigue.2006.03.002 ↗
28. Prediction of pipeline fatigue crack propagation under rockfall impact based on multilayer perceptron10.1016/j.ress.2023.109772 ↗
29. A mathematical theory of communication10.1002/j.1538-7305.1948.tb01338.x ↗
30. Sohn · 2003 · 16
31. Application of maximum entropy principle for reliability-based design optimization10.1007/s00158-008-0299-3 ↗
32. A novel approach to uncertainty analysis using methods of hybrid dimension reduction and improved maximum entropy10.1007/s00158-019-02294-8 ↗
33. Roles of microstructure in fatigue crack initiation10.1016/j.ijfatigue.2009.10.005 ↗
34. Probabilistic modeling of fatigue life distribution and size effect of components with random defects10.1016/j.ijfatigue.2019.05.005 ↗
35. Probabilistic modeling of uncertainties in fatigue reliability analysis of turbine bladed disks10.1016/j.ijfatigue.2020.105912 ↗
36. Backwards statistical inference method for P-S-N curve fitting with small-sample experiment data10.1016/j.ijfatigue.2014.01.006 ↗
37. P-S-N curve fitting method based on sample aggregation principle10.1007/s11668-019-00586-1 ↗
38. Probabilistic physics-guided machine learning for fatigue data analysis10.1016/j.eswa.2020.114316 ↗
39. Identifying the probability distribution of fatigue life using the maximum entropy principle10.3390/e18040111 ↗
40. Maximum entropy density estimation from fractional moments10.1081/STA-120018189 ↗
41. Engineering application of the maximum entropy reliability theory
42. MFLP-PINN: A physics-informed neural network for multiaxial fatigue life prediction10.1016/j.euromechsol.2022.104889 ↗
43. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures10.1016/j.engfracmech.2021.108130 ↗
44. Shimokawa · 1985
45. A neural network approach to fatigue life prediction10.1016/j.ijfatigue.2010.09.003 ↗
46. ISO 12107–2003 Metallic materials Fatigue testing Statistical planning and analysis of data [S].
47. A study on sensitivity of the probability-weighted moments method on the choice of the plotting position formula10.1016/0022-1694(94)02642-O ↗
48. Reliability fatigue life and A new S–N curve model of composite laminates under tensile-tensile fatigue load10.1007/s10443-020-09847-x ↗