Data-Driven Lamb-Wave-Based Approach to Detect Multiple Structural Damages

AIAA Journal ◽  
2021 ◽  
pp. 1-7
Author(s):  
Sachin Kumar ◽  
Mohammed Rabius Sunny
Keyword(s):  
Lamb Wave ◽  
Data Driven

Coherent, data-driven Lamb wave localization under environmental variations

2015 ◽  
Author(s):  
Joel B. Harley ◽  
Chang Liu ◽  
Irving J. Oppenheim ◽  
David W. Greve ◽  
José M.F. Moura
Keyword(s):  
Lamb Wave ◽  
Data Driven ◽  
Wave Localization ◽  
Environmental Variations

scholarly journals Hybrid Approach of Data-Driven and Physics-based Methods for Estimation and Prediction of Fatigue Crack Growth

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyeon Bae Kong ◽  
Soo-Ho Jo ◽  
Joon Ha Jung ◽  
Jong M. Ha ◽  
Yong Chang Shin ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Length ◽  
Crack Growth ◽  
Lamb Wave ◽  
Loading Condition ◽  
Growth Patterns ◽  
Data Driven ◽  
Model Parameters ◽  
Length Estimation

This paper aims to develop a hybrid method to estimate the fatigue crack growth of an aluminum lap joint specimen with and without Lamb wave signals. The proposed method is validated on the two validation specimens (T7 and T8), using the training data sets of six different specimens (T1-T6). Each validation data set includes crack length estimation of few loading cycles with the given Lamb wave signals, followed by crack estimation without the signals. First, the crack length estimation using the signals for T7 and T8 sets was performed by the data-driven based method. A set of features was extracted from the preprocessed signals. Then, a random forest model was used to estimate crack lengths with grid search-based feature selection and hyper-parameter optimization. Next, different approaches were used to estimate the crack length without the signals, since T7 and T8 were tested under different loading conditions. Assuming that the homogeneous constant loading condition leads to a similar fatigue crack growth patterns, an ensemble prognostics approach with simplified particle filter-based weight update was used to predict the crack lengths of T7 specimen. In contrast, Walker’s equation model-based approach was chosen for T8 specimen as it was tested under a different loading condition. Considering the uncertainties of the model parameters, Walker’s equation models were generated by Monte Carlo methods. The average of generated models were used to predict the remaining crack lengths of T8 specimen. The proposed method led to Top 3 in 2019 PHM Conference Data Challenge.

scholarly journals Uncertainty quantification for impact location and force estimation in composite structures

2021 ◽  
pp. 147592172110202
Author(s):  
Aldyandra Hami Seno ◽  
MH Ferri Aliabadi
Keyword(s):  
Uncertainty Quantification ◽  
Lamb Wave ◽  
Measurement Errors ◽  
Composite Structures ◽  
Data Driven ◽  
Maximum Force ◽  
Reliable Estimate ◽  
Reference Database ◽  
Force Estimation ◽  
Impact Location

Structural health monitoring of impact location and severity using Lamb waves has been proven to be a reliable method under laboratory conditions. However, real-life operational and environmental conditions (vibration noise, temperature changes, different impact scenarios, etc.) and measurement errors are known to generate variation in Lamb wave features which may significantly affect the accuracy of these estimates. Therefore, these uncertainties should be considered, as a deterministic approach may lead to erroneous decisions. In this article, a novel data-driven stochastic Kriging-based method for impact location and maximum force estimation, that is able to reliably quantify the output uncertainty is presented. The method utilises a novel modification of the kriging technique (normally used for spatial interpolation of geostatistical data) for statistical pattern matching and uncertainty quantification using Lamb wave features to estimate the location and maximum force of impacts. The data was experimentally obtained from a composite panel equipped with piezoelectric sensors. Comparison with a deterministic benchmark method developed in prior studies shows that the proposed method gives a more reliable estimate for experimental impacts under various simulated environmental and operational conditions by estimating the uncertainty. The developed method highlights the suitability of data-driven methods for uncertainty quantification, by taking advantage of the relationship between data points in the reference database that is a mandatory component of these methods (and is often seen as a disadvantage). By quantifying the uncertainty, there is more information for operators to reliably locate impacts and estimate the severity, leading to robust maintenance decisions.

scholarly journals Identification and Compensation Technique of Non-Uniform Temperature Field for Lamb Wave-and Multiple Sensors-Based Damage Detection

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2930 ◽  
Author(s):  
Hu Sun ◽  
Junyan Yi ◽  
Yu Xu ◽  
Yishou Wang ◽  
Xinlin Qing
Keyword(s):  
Temperature Field ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Temperature Compensation ◽  
Data Driven ◽  
Uniform Temperature ◽  
Uniform Temperature Field ◽  
Compensation Technique

Lamb wave-based damage detection for large-scale composites is one of the most prosperous structural health monitoring technologies for aircraft structures. However, the temperature has a significant effect on the amplitude and phase of the Lamb wave signal so that temperature compensation is always the focus problem. Especially, it is difficult to identify the damage in the aircraft structures when the temperature is not uniform. In this paper, a compensation method for Lamb wave-based damage detection within a non-uniform temperature field is proposed. Hilbert transform and Levenberg-Marquardt optimization algorithm are developed to extract the amplitude and phase variation caused by the change of temperature, which is used to establish a data-driven model for reconstructing the reference signal at a certain temperature. In the temperature compensation process, the current Lamb wave signal of each exciting-sensing path under the estimated structural condition is substituted into the data-driven model to identify an interpolated initial temperature field, which is further processed by an outlier removing algorithm to eliminate the effect of damage and get the actual non-uniform temperature field. Temperature compensation can be achieved by reconstructing the reference signals within the identified non-uniform temperature field, which are used to compare with the current acquired signals for damage imaging. Both simulation and experiment were conducted to verify the feasibility and effectiveness of the proposed non-uniform temperature field identification and compensation technique for Lamb wave-based structural health monitoring.

Lamb Wave Tomography and Its Application in Pipe Erosion/Corrosion Monitoring

1996 ◽  
Vol 8 (1) ◽  
pp. 189-197
Author(s):  
J. Pei ◽  
M. I. Yousuf ◽  
F. L. Degertekin ◽  
B. V. Honein ◽  
B. T. Khuri-Yakub
Keyword(s):  
Lamb Wave ◽  
Corrosion Monitoring ◽  
Erosion Corrosion ◽  
Wave Tomography
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