A model assessment method for predicting structural fatigue life using Lamb waves

Wang, Dengjiang; He, Jingjing; Guan, Xuefei; Yang, Jann N.; Zhang, Weifang

doi:10.1016/j.ultras.2017.11.017

Cited by 42 publications

(15 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performances of different excitation waveforms are discussed by using POD and average error; the reliability of the detection model and the accuracy of the detection model are evaluated, respectively [ 31 ]. The POD model will be derived first.…”

Section: Detection Model Establishmentmentioning

confidence: 99%

A Crack Size Quantification Method Using High-Resolution Lamb Waves

Yang

Zhang

2021

Sensors

Self Cite

View full text Add to dashboard Cite

Traditional tone burst excitation cannot attain a high output resolution, due to the time duration. The received signal is much longer than that of excitation during the propagation, which can increase the difficulty of signal processing, and reduce the resolution. Therefore, it is of significant interest to develop a general methodology for crack quantification through the optimal design of the excitation waveform and signal-processing methods. This paper presents a new crack size quantification method based on high-resolution Lamb waves. The linear chirp (L-Chirp) signal and Golay complementary code (GCC) signal are used as Lamb wave excitation signals. After dispersion removal, these excitation waveforms, based on pulse compression, can effectively improve the inspection resolution in plate-like structures. A series of simulations of both healthy plates and plates with different crack sizes are performed by Abaqus CAE, using different excitation waveforms. The first wave package of the S0 mode after pulse compression is chosen to extract the damage features. A multivariate regression model is proposed to correlate the damage features to the crack size. The effectiveness of the proposed crack size quantification method is verified by a comparison with tone burst excitation, and the accuracy of the crack size quantification method is verified by validation experiments.

show abstract

Section: Detection Model Establishmentmentioning

confidence: 99%

A Crack Size Quantification Method Using High-Resolution Lamb Waves

Yang

Zhang

2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to the reflection and scattering of ultrasonic waves at the crack location, a part of the wave energy will be dispersed. The longer the crack is, the more energy will be dispersed (Wang, He, Guan, Yang, & Zhang, 2018;He, Huo, Guan, & Yang, 2020). As a result, the energy of the received ultrasonic signals will decrease as the crack size increases.…”

Section: Feature Extractionmentioning

confidence: 99%

“…They contribute to about 90% of the failures of metallic structures (Campbell, 2018). If fatigue cracks are not detected and addressed early, they will jeopardize the longterm durability and reliability of structural systems (Wang, He, Guan, Yang, & Zhang, 2018). For example, the Eschede train accident was caused by a fatigue crack in one of the train wheels (Esslinger, Kieselbach, Koller, & Weisse, 2004).…”

Section: Introductionmentioning

confidence: 99%

Structure Fatigue Crack Length Estimation and Prediction Using Ultrasonic Wave Data Based on Ensemble Linear Regression and Paris’s Law

Rao

Yang

Wei

et al. 2021

IJPHM

View full text Add to dashboard Cite

This paper presents methods for the 2019 PHM Conference Data Challenge developed by the team named "Angler". This Challenge aims to estimate the fatigue crack length of a type of aluminum structure using ultrasonic signals at the current load cycle and to predict the crack length at multiple future load cycles (multiple-step-ahead prediction) as accurately as possible. For estimating crack length, four crack-sensitive features are extracted from ultrasonic signals, namely, the first peak value, root mean square value, logarithm of kurtosis, and correlation coefficient. An ensemble linear regression model is presented to map these features and their second-order interactions with the crack length. The Best Subset Selection method is employed to select the optimal features. For predicting crack length, variations of the Paris’ law are derived to describe the relationships between the crack length and the number of load cycles. The material parameters and stress range of Paris’ law are learned using the Genetic Algorithm. These parameters will be updated based on the previous-step predicted crack length. After that, the crack length corresponding to a future load cycle number for either the constant amplitude load case or variable amplitude load case is predicted. The presented methods achieved a score of 16.14 based on the score-calculation rule provided by the Data Challenge committees, and was ranked third best among all participating teams.

show abstract

“…Using the method of Markov chain Monte Carlo (MCMC), samples can directly be drawn from the posterior distribution without knowing the normalizing constant p(x) = p(φ)p(x|φ)dφ in Equation (1). The Bayesian method has been successfully demonstrated in all important disciplines [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference under Small Sample Sizes Using General Noninformative Priors

Wang

Huang

et al. 2021

Mathematics

Self Cite

View full text Add to dashboard Cite

This paper proposes a Bayesian inference method for problems with small sample sizes. A general type of noninformative prior is proposed to formulate the Bayesian posterior. It is shown that this type of prior can represent a broad range of priors such as classical noninformative priors and asymptotically locally invariant priors and can be derived as the limiting states of normal-inverse-Gamma conjugate priors, allowing for analytical evaluations of Bayesian posteriors and predictors. The performance of different noninformative priors under small sample sizes is compared using the likelihood combining both fitting and prediction performances. Laplace approximation is used to evaluate the likelihood. A realistic fatigue reliability problem was used to illustrate the method. Following that, an actual aeroengine disk lifing application with two test samples is presented, and the results are compared with the existing method.

show abstract

A model assessment method for predicting structural fatigue life using Lamb waves

Cited by 42 publications

References 49 publications

A Crack Size Quantification Method Using High-Resolution Lamb Waves

A Crack Size Quantification Method Using High-Resolution Lamb Waves

Structure Fatigue Crack Length Estimation and Prediction Using Ultrasonic Wave Data Based on Ensemble Linear Regression and Paris’s Law

Bayesian Inference under Small Sample Sizes Using General Noninformative Priors

Contact Info

Product

Resources

About