journal article·2023-05

Fatigue damage diagnosis and prognosis for 2024 aluminum plates with center holes: A strain monitoring approach

Liang Han, Xiaofan He, Yu Ning, Yanjun Zhang, Yan Zhou

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Fatigue and fracture mechanics · Non-Destructive Testing Techniques · Structural Health Monitoring Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (54)

Show source-provided references
1. Kim · 2016
2. Yuan · 2007
3. Prognostics and health management: a review on data driven approaches10.1155/2015/793161 ↗
4. Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications10.1016/j.ymssp.2013.06.004 ↗
5. Review of fatigue monitoring of agile military aircraft10.1046/j.1460-2695.2000.00330.x ↗
6. Guided wave based structural health monitoring: A review10.1088/0964-1726/25/5/053001 ↗
7. Tua P S, Quek S T, Wang Q. Detection of crack in thin cylindrical pipes using piezo-actuated lamb waves. In: Proc. SPIE 5765, Smart Structures and Materials 2005: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems. International Society for Optics and Photonics; 2005. 10.1117/12.596074.10.1117/12.596074 ↗
8. Monitoring and instantaneous evaluation of fatigue crack using integrated passive and active laser thermography10.1016/j.optlaseng.2019.02.001 ↗
9. Development of a “stick-and-detect” wireless sensor node for fatigue crack detection10.1177/1475921716666532 ↗
10. Piezoelectric sensor-based health monitoring of railroad tracks using a two-step support vector machine classifier10.1061/(ASCE)1076-0342(2008)14:1(80) ↗
11. On-line crack prognosis in attachment lug using Lamb wave-deterministic resampling particle filter-based method10.1088/1361-665X/aa7168 ↗
12. On-line prognosis of fatigue cracking via a regularized particle filter and guided wave monitoring10.1016/j.ymssp.2019.05.022 ↗
13. On-line updating Gaussian process measurement model for crack prognosis using the particle filter10.1016/j.ymssp.2020.106646 ↗
14. Dynamic Bayesian network for aircraft wing health monitoring digital twin10.2514/1.J055201 ↗
15. Novel method for in situ damage monitoring during ultrasonic fatigue testing by the advanced acoustic emission technique10.1016/j.ijfatigue.2020.105918 ↗
16. Incorporation of fiber Bragg grating sensors in additive manufactured Acrylonitrile butadiene styrene for strain monitoring during fatigue loading10.1016/j.ijfatigue.2021.106485 ↗
17. An inverse elastic method of crack identification based on sparse strain sensing sheet10.1177/1475921720939518 ↗
18. Calorimetric analysis of dissipative and thermoelastic effects associated with the fatigue behavior of steels10.1016/S0142-1123(03)00171-3 ↗
19. Using acoustic emission to understand fatigue crack growth within a single load cycle10.1016/j.engfracmech.2018.03.012 ↗
20. Development and validation of a strain-based structural health monitoring system10.1016/j.ymssp.2008.03.006 ↗
21. Thermoelastic stress analysis and structural health monitoring: an emerging nexus10.1177/1475921714548936 ↗
22. Kaneko H, Furukawa T. Operational loads regression equation development for advanced fighter aircraft. Paper ICAS 24th International Congress of the Aeronautical Sciences. Japan; 2004.
23. Timothy F, Devinder M, Iain H. F-35 joint strike fighter structural prognosis and health management an overview. ICAF 2009, Bridging the Gap between Theory and Operational Practice. Dordrecht: Springer; 2009. 10.1007/978-90-481-2746-7_68.10.1007/978-90-481-2746-7_68 ↗
24. Eurofighter 2000: an integrated approach to structural health and usage monitoring
25. Tikka J, Salonen T. Practical Experience of Neural Network Based Fatigue Life Monitoring. In: Siljander, A. (Ed.) Embracing the future-respecting the past; supporting aging fleets with new technologies. Proceedings of the 34th Conference and the 28th Symposium of the International Committee on Aeronautical Fatigue and Structural Integrity; 2015 June 1-5; Helsinki, Finland;2015. p. 879-888.
26. Introduction to service of an artificial neural network based fatigue monitoring system
27. Spreading of Rear Suspension Fatigue Cracks by Strain Real-time Monitoring
28. Hewitt R L, Hiscocks R J. Experiences with on-line data trend monitoring on a Full-Scale aircraft fatigue test. In Proceedings of ICAF2003-Fatigue of Aeronautical Structures as an Engineering Challenge; 2004. p. 89-108.
29. A research on fatigue crack growth monitoring based on multi-sensor and data fusion
30. FEM based fatigue crack growth predictions for spar of light aircraft under variable amplitude loading10.1016/j.engfailanal.2012.07.003 ↗
31. Extended finite element method (XFEM) applied to aircraft duralumin spar fatigue life estimation
32. Grbovic M A, Rasuo B. Chapter 2: Use of Modern Numerical Methods for Fatigue Life Predictions (pp. 31-74). In: Recent Trends in Fatigue Design, (Ricardo Branco, Editor), Series: Mechanical Engineering Theory and Applications. New York: Nova Science Publishers; 2014.
33. Aircraft duraluminum wing spar fatigue testing10.5937/fmet1704531P ↗
34. Orchard M E. A particle filtering-based framework for online fault diagnosis and failure prognosis [dissertation]. Atlanta: Georgia Institute of Technology. Dissertation; 2007.10.1109/MED.2007.4433871 ↗
35. Wu X R. Handbook of mechanical properties of aircraft structural metals, vol. Ⅱ: Damage tolerance. Beijing (China): Press of Aeronautics and Astronautics Industry; 1996.
36. Effect of reinforcement ratio on the flexural performance of hybrid FRP reinforced concrete beams10.1016/j.compositesb.2016.09.054 ↗
37. A multistage adaptive thresholding method10.1016/j.patrec.2004.11.003 ↗
38. Rasmussen · 2003 · 63
39. An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission10.1016/j.ymssp.2016.07.039 ↗
40. Active-sensing structural health monitoring via statistical learning: an experimental study under varying damage and loading states
41. A critical analysis of crack propagation laws10.1115/1.3656900 ↗
42. The Statistical Nature of Fatigue Crack Propagation10.1115/1.3443666 ↗
43. Particle Filtering Prognostic Estimation of the Remaining Useful Life of Nonlinear Components10.1016/j.ress.2010.08.009 ↗
44. Prognostics Uncertainty Reduction by Fusing On-line Monitoring Data Based on a State-space-based Degradation Model10.1016/j.ymssp.2013.08.022 ↗
45. Murakami Y, Keer L M. Stress intensity factors handbook, vol. 3. Journal of Applied Mechanics, 1993; 60(4): 1063-1063. 10.1115/1.2900983.10.1115/1.2900983 ↗
46. Approximate determination of stress intensity factor for multiple surface cracks10.5937/fmet1801039K ↗
47. Using a Simple Crack Growth Model in Predicting Remaining Useful Life10.2514/1.C031808 ↗
48. Damage diagnosis and prognosis in composite double cantilever beam coupons by particle filtering and surrogate modelling10.1177/1475921720960067 ↗
49. The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing10.1002/stc.290 ↗
50. Progressive damage identification using dual extended Kalman filter10.1007/s00707-016-1590-9 ↗
51. ASTM E466-21: 2021 Standard Practice for Conducting Force Controlled Constant Amplitude Axial Fatigue Tests of Metallic Materials. 10.1520/E0466-21.
52. Artificial neural networks in spectrum fatigue life prediction of composite materials10.1016/j.ijfatigue.2006.03.004 ↗
53. A fatigue life prediction approach for laser-directed energy deposition titanium alloys by using support vector regression based on pore-induced failures10.1016/j.ijfatigue.2022.106748 ↗
54. Application of Stepwise Regression to Non-Linear Estimation10.1080/00401706.1968.10490535 ↗