Ultrasonic guided wave imaging with deep learning: Applications in corrosion mapping

Wang, Xiaocen; Lin, Min; Li, Jian; Tong, Junkai; Huang, Xinjing; Liang, Lin; Fan, Zheng; Yang, Liu

doi:10.1016/j.ymssp.2021.108761

Cited by 40 publications

(15 citation statements)

References 33 publications

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“…27 Ultrasonic inspection has become increasingly popular in recent years with the use of artificial intelligence (AI) and signal processing. Examples include support vector machine algorithms for detecting defects from ultrasonic signals, 28,29 artificial neural networks (ANNs) for ultrasonic guided wave classification, 30,31 convolutional neural networks (CNN) based ultrasonic testing, 32,33 recurrent neural networks (RNN) and Lamb waves, 34,35 relevance vector machine (RVM), 36,37 k-nearest neighbors (kNNs), etc. All those methods can provide high accuracy in the particular data being used, the CNN and RNN are most suitable for image classification, while RVM and kNN methods show high performance with hierarchical data, and ANN and deep neural network (DNN) are suitable for the vectorial data inputs.…”

Section: Introductionmentioning

confidence: 99%

A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks

et al. 2023

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The heat exchanger (HE) is an important component of almost every energy generation system. Periodic inspection of the HEs is particularly important to keep high efficiency of the entire system. In this paper, a novel ultrasonic water immersion inspection method is presented based on circumferential wave (CW) propagation to detect defective HE. Thin patch-type piezoelectric elements with multiple resonance frequencies were adopted for the ultrasonic inspection of narrow-spaced HE in an immersion test. Water-filled HE was used to simulate defective HE because water is the most reliable indicator of the defect. The HE will leak water no matter what the defect pattern is. Furthermore, continuous wavelet transform (CWT) was used to investigate the received CW, and inverse CWT was applied to separate frequency bands corresponding to the thickness and lateral resonance modes of the piezoelectric element. Different arrangements of intact and leaky HE were tested with several pairs of thin piezoelectric patch probes in various instrumental setups. Also, direct waveforms in the water without HE were used as reference signals, to indicate instrumental gain and probe sensitivity. Moreover, all filtered CW corresponding to resonance modes together with the direct waveforms in the water were used to train the deep neural networks (DNNs). As a result, an automatic HE state classification method was obtained, and the accuracy of the applied DNN was estimated as 99.99%.

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Section: Introductionmentioning

confidence: 99%

A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks

et al. 2023

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“…CNN has been implemented in several cases, such as detecting rust on pipes [9], detecting defects on hull surfaces [10], and detecting biofouling inspection on Berth [11]. Wang et al [12] conducted a corrosion detection study using CNN and used two methods: offline training and online training. Offline training used advanced modeling data to synchronize detection signals and speed maps.…”

Section: Introductionmentioning

confidence: 99%

Automated Corrosion Detection on Steel Structures Using Convolutional Neural Network

Effendi

Atmaja

Wahjudi

et al. 2023

JMES Int. Journal of Mech. Eng. Sc.

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Steel is a widely used material in industry and construction. The tensile and compressive strengths of steel are relatively high compared to other materials. On the other hand, low corrosion resistance is the main weakness of steel, which can encourage steel deterioration and fatal accidents for the user. Furthermore, regular visual inspection by a human should be performed to prevent catastrophic incidents. However, human visual inspection increases the risk of work accidents and reduces work effectiveness. Therefore, a drone with a camera is one solution to increase efficiency, increase security levels, and minimize difficulties or risks during corrosion detection. In this research, the drone was used to capture corroded video of a construction structure. The convolutional neural network (CNN) method was used to detect the location of the corroded images. This study was conducted on Surabaya's Petekan bridge using the Mobilenet V1 SSD pre-training model. In this study, the distance between a drone and the detected object varied between 1 and 2 m. Next, the drone speed was varied into 0.6 m/s, 0.9 m/s, and 1.3 m/s. As a result, CNN could detect corrosion on the surface of steel materials. The best accuracy was 84.66% with a minimum total loss value of 1.673 by applying 200 images, 200000 epochs, batch size at 4, learning rate at 0.

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“…Investigations of ultrasonic technique for detection and imaging of corrosion have been performed [ 9 , 10 , 11 ]. Ultrasonic testing has proved to be able to locate corrosion damage and estimate its size [ 5 ]; nevertheless, the ultrasound source probe typically installed on the material surface (together with a set of receivers) implies surface cleaning, a strong electric energy source, and possible influence of surface roughness on the test results.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Acoustic Emission Monitoring of Corrosion under Marine Growth

Alkhateeb

Riccioli

Morales³

et al. 2022

Sensors

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Offshore support structures and mooring systems are predominantly subject to corrosion and fatigue. These structures are typically covered with marine growth of various types. Conventional inspection methods for assessment of the structural integrity require access to the cleaned surface of these structures; however, the cleaning process is highly undesirable from the technical, economical, and environmental points of view. This paper highlights research on feasibility assessment of detection and localization of corrosion damage under marine growth using acoustic emission (AE). Experiments were conducted on two carbon steel plates, one baseline sample and one covered with artificially fabricated marine growth. The results of accelerated corrosion experiments suggest that corrosion-induced ultrasound signals can be detected with satisfactory signal-to-noise ratio using non-contact AE sensors. Ultrasound waves passing through marine growth showed around 12 dB drop in amplitude when compared to the base plate. A localization algorithm for corrosion induced-ultrasound signals was successfully implemented.

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Ultrasonic guided wave imaging with deep learning: Applications in corrosion mapping

Cited by 40 publications

References 33 publications

A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks

A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks

Automated Corrosion Detection on Steel Structures Using Convolutional Neural Network

Non-Contact Acoustic Emission Monitoring of Corrosion under Marine Growth

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