An adaptive probabilistic data-driven methodology for prognosis of the fatigue life of composite structures

Eleftheroglou, Nick; Zarouchas, Dimitrios; Benedictus, Rinze

doi:10.1016/j.compstruct.2020.112386

Cited by 34 publications

(26 citation statements)

References 16 publications

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“…Data-driven approaches, on the other hand, make use of monitoring data acquired during the whole life of the system (and with different levels of damage) and are preferred when a physical model of the damage process is unavailable, due for instance to the complexity of the process itself or the variability of operational conditions. Data-driven approaches require a large set of monitoring data and are generally based on probabilistic models that aim to identify trends by learning from the available data [48].…”

Section: Real Time Estimation Of the Damage Of Structural Componentsmentioning

confidence: 99%

“…In [51], the authors propose a probabilistic data-driven method for the RUL estimation of carbon/epoxy specimens subjected to a constant amplitude fatigue loading. The RUL model is based on Non-Homogeneous Hidden Semi Markov model (NHHSMM), which was improved in [48] by including adaptive features based on monitored data. In [52], a statistical life prediction method of composite structures is presented.…”

Section: Real Time Estimation Of the Damage Of Structural Componentsmentioning

confidence: 99%

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Motorcycle Structural Fatigue Monitoring Using Smart Wheels

et al. 2020

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The paper is devoted to the measurement and to the processing of load spectra of forces and moments acting at the wheel hub of a motorcycle. Smart wheels (SWs) have been specifically developed for the scope. Throughout the paper, the extreme case of a race motorcycle is considered. Accurate load spectra were measured in two race circuits. Standardized load spectra are derived by processing measured data. A way to easily generalize the measured load spectra is proposed for the first time for motorcycles. Several loading conditions, related to the motorcycle straight line motion, cornering, curb hit and gear shift, are identified and extracted from the experimental measures. For each loading condition, by means of simple semi-analytical models (SAMs), a relationship is found between the vertical force on the wheel, the tilt angle of the motorcycle and the remaining forces and moments acting at the wheel hub. Such relationships are nothing else than the standardized load spectra. Additionally, a simple and efficient method based on smart wheels for real-time structural monitoring is proposed. Standardized load spectra prove to provide consistent results even when compared to real-time structural monitoring data. By means of the presented smart wheels, advanced lightweight motorcycle construction is enabled by derivation of standardized load spectra or real time estimation of the damage of structural components.

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Section: Real Time Estimation Of the Damage Of Structural Componentsmentioning

confidence: 99%

Section: Real Time Estimation Of the Damage Of Structural Componentsmentioning

confidence: 99%

Motorcycle Structural Fatigue Monitoring Using Smart Wheels

et al. 2020

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“…Introduced by Bogdanoff, 24 the Markov chain considers damage distribution in cycles, making it possible to quantify damage progression and not just estimate lifetime. It has been applied to composite materials 25–27 while showing life prediction skills. The models can be made more complex by using a time transformation, 25,28 by optimizing the number of parameters, 29 and/or by calculating them iteratively 30 .…”

Section: Introductionmentioning

confidence: 99%

“…All three of these techniques increase the precision of an already well‐fitted model. The main inputs to a Markov chain are numerical estimations of sample damage 27 . Any damage quantification method that is adaptable to numerical values can be used.…”

Section: Introductionmentioning

confidence: 99%

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Fatigue of short‐natural‐fiber‐reinforced high‐density polyethylene: Stochastic modeling of single‐gear‐tooth bending

Blais

Toubal

2021

Fatigue Fract Eng Mat Struct

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A Markov chain model was compared to experimental data in the testing of a natural‐fiber‐reinforced composite material proposed for low‐torque gearing applications. The material is tested on a single gear tooth bench to assess multiple damage measurement methods. The model was coupled to three indices of single gear tooth damage: cracking monitored with a high resolution camera, residual load measured with the fatigue machine load cell, and acoustic emission detected by piezoelectric sensors. The model has predictive skill and contributed to better understanding of how damage to the material evolved during the fatigue tests.

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A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

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An adaptive probabilistic data-driven methodology for prognosis of the fatigue life of composite structures

Cited by 34 publications

References 16 publications

Motorcycle Structural Fatigue Monitoring Using Smart Wheels

Motorcycle Structural Fatigue Monitoring Using Smart Wheels

Fatigue of short‐natural‐fiber‐reinforced high‐density polyethylene: Stochastic modeling of single‐gear‐tooth bending

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

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