A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels

Ebrahimkhanlou, Arvin; Dubuc, Brennan; Salamone, Salvatore

doi:10.1016/j.ymssp.2019.04.050

Cited by 114 publications

(47 citation statements)

References 21 publications

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“…In recent years, networks of deep learning have risen and are being used in the analysis of big data from the SHM system, as well as the infrastructures' intelligent operation and maintenance. With the help of the approaches of deep learning, the structural damage can be more accurately identified from one‐dimensional data 37–41 . Combined with computer vision technology, the networks of deep learning represented by convolutional neural network (CNN) are developed to conduct the smart detection of the cracking, corrosion, and looseness for various structural components 42–47 .…”

Section: Introductionmentioning

confidence: 99%

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Zhao

Wei

et al. 2020

Struct Control Health Monit

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Nonlinear mapping of the fuzzy relation exists between structural inputs and outputs, as well as between structural global and local response. It is difficult for the numerical simulation to introduce the nonlinear effects of the variability of loading effects and the uneven deterioration of structure. The big monitoring data make it feasible to mine these nonlinear effects, and the network of deep learning is a good tool to establish the nonlinear mapping model between the multi-source monitoring data. Based on the temperature, strain, and dynamic displacement data from the structural health monitoring (SHM) system of an in-service bridge, the deep learning regression network of long short-term memory (LSTM) is designed and trained, which is used for modeling the nonlinear mapping between the structural input-output and the structural global-local response. The digital model of the nonlinear mapping of the temperature to temperature-induced strain, dynamic displacement to vehicleinduced strain, and vehicle-induced strain to dynamic displacement is established by the deep learning of LSTM regression network. The established nonlinear mapping model can be used to recover the abnormal and missing data with multi-source data, and it digitally linked the structural input-output and the structural global-local response. Then, the utilization of SHM data analytics will be enhanced, and the digital modeling for the in-service state of the bridge structure will be fast implemented.

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Section: Introductionmentioning

confidence: 99%

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Zhao

Wei

et al. 2020

Struct Control Health Monit

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“…Thus, it needs a more robust and an effective framework to weaken the part of previous feature extraction and to achieve an automatic recognition function. AE signal processing system based on deep learning is gradually established [18,29]. One main difference between deep networks and traditional shallow neural networks is that the input to deep networks can be signals or their transformations, rather than a few features extracted from them [22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…(II) For a few representative characteristics and patterns in AE waveforms, deep learning can automatically recognize these features. (III) It contains primely anti-overfitting performance and is especially suitable for the application with few training datasets [18,19,[29][30][31][32][33]. Compared with conventional signal processing-based techniques (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Yang

2020

IEEE Access

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Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors' knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a tenfold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5") and 48 mm (1.9"), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5") and 67 mm (2.1"), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with singlesensor. INDEX TERMS acoustic emission (AE); sources localization and identification; stacked denoising autoencoders (SDAE); metallic steel plates; structural health monitoring

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“…They proposed a novel, single-sensor acoustic emission (AE) source localization algorithm [18,19], with the use of the total least squares method and a multipath model. In addition, a deep-learning-based framework to localize an acoustic emission source in plate-like structures that have complex geometric was also proposed by them [20,21]. All the methods can achieve good results in experimental conditions, while some further study can also be made in more complex conditions and materials.…”

Section: Introductionmentioning

confidence: 99%

An Impact Location Algorithm for Spacecraft Stiffened Structure Based on Posterior Possibility Correlation

Zeng

Sun

et al. 2020

Sensors

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In order to ensure the safety of spacecrafts in orbit, impact location is an important part of structural health monitoring systems. In this paper, an impact location algorithm based on posterior probability correlation is proposed to solve the problem, that is, the impact point in the stiffened structure of a spacecraft is difficult to locate. The algorithm combines the Gaussian cross-correlation possibility weight method and the Bayesian posterior probability method. The cross-correlation possibility weight superposition based on grids was used to reduce the dependence of the accuracy of time difference extraction. Gaussian and normalized fitting were used to compensate the reflection, modal transformation, and amplitude attenuation of a stiffened plate. The location result was further optimized by the posterior probability. The proposed algorithm can be applied to the impact source localization of complex stiffened plate structures. The experiment results showed that the average location error can be 2.57 cm with proper sensor network schemes.

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A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels

Cited by 114 publications

References 21 publications

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

An Impact Location Algorithm for Spacecraft Stiffened Structure Based on Posterior Possibility Correlation

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