Fatigue life estimation of structures under statistically and spectrally similar variable amplitude loading

Li, He Wen Xuan; Chelidze, David

doi:10.1016/j.ymssp.2021.107856

Cited by 8 publications

(4 citation statements)

References 60 publications

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“…(5) The accuracy and reliability of the GIPSW algorithm are validated using a series of numerical experiments that resemble real-world damage dynamics and response complexity of nonlinear systems. (6) The algorithm is applied to a set of acquired displacement data recorded from nonlinear magnetoelastic beam experiments, showing that the raster angle significantly affects the crack propagation rate. This shows its practical use in the nondestructive monitoring of nonlinear systems.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, it facilitates damage prognosis and antifatigue design, especially when the structures are subjected to variable amplitude irregular loads. [3][4][5][6] By geometrically describing the damageinduced deformation of the implied underlying dynamical system, this study establishes a sampling theory for a new data-driven fatigue damage monitoring method that reliably characterizes the latent damage dynamics explicitly from the observable system response.…”

Section: Introductionmentioning

confidence: 92%

“…This selection also ensures that no excessive samples are drawn from the predefined sample space O. The samples of the GIPSW parameters (i.e., nm, nn, nb, k) are drawn through a Sobol quasi-random set and evaluated through Equation ( 16) with a fixed set of temporal averaging parameters rl = 2 6 and nr ensuring 0% overlapping. The first dominant smooth orthogonal coordinate (SOC) from each PSW estimation, obtained from SOD, is treated as the estimate of the damage-time history and is scaled to its original range.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Geometry-informed phase space warping for reliable fatigue damage monitoring

Chelidze

2023

Structural Health Monitoring

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This paper presents a new fatigue damage detection and monitoring approach using a geometry-informed implementation of phase space warping (PSW). The proposed method is based on continuous-time PSW theory and geometric constructs, which clarifies the relationship between the deformation of the reconstructed phase flow and the underlying damage evolution. A discrete-time approximation to the continuous-time theory is established for practical applications. The practical geometry-informed PSW (GIPSW) algorithm is developed with integrated geometry-informed heuristics and global sensitivity analysis to monitor fatigue damage evolution accurately. The proposed method is validated through numerical experiments simulating nonlinear systems with varying fatigue damage dynamics, exhibiting distinct response complexities. The results show that the GIPSW improves the monitoring accuracy by at least 41.4% and can achieve maximally four-orders-of-magnitude-lower monitoring error compared with the conventional PSW algorithm. The GIPSW is also applied in physical experiments exploring raster-angle-affected fatigue damage dynamics in 3D-printed materials. The estimated hidden-fatigue damage-time history reveals distinct crack propagation rates differentiated by the raster angles and can be used for damage prognosis and modeling the fatigue mechanisms. The critical inflection points identified in the incremental damage-time histories detect the crack growth phase transitions as early as 0.17 of the total time to failure, which can be used for early damage awareness.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Numerical Experimentsmentioning

confidence: 99%

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Geometry-informed phase space warping for reliable fatigue damage monitoring

Chelidze

2023

Structural Health Monitoring

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“…Morrow and Smith-Watson-Topper models. In addition, the strain-life models excluded the sequence of load cycle effect in assessing the fatigue life of components and structures [13]. The advantage of using the load cycle sequence effect-based model, namely effective strain damage (ESD), is to predict fatigue life in various materials associated with a variety of loading data.…”

Section: Introductionmentioning

confidence: 99%

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

Karam Singh,

Abdullah,

Abdullah

et al. 2024

Eksploatacja i Niezawodność – Maintenance and Reliability

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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.

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Joint discriminative learning and classification for monitoring flow patterns of supersonic inlet

Zhao

Tan

2022

Aerospace Science and Technology

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Fatigue life estimation of structures under statistically and spectrally similar variable amplitude loading

Cited by 8 publications

References 60 publications

Geometry-informed phase space warping for reliable fatigue damage monitoring

Geometry-informed phase space warping for reliable fatigue damage monitoring

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

Joint discriminative learning and classification for monitoring flow patterns of supersonic inlet

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