Deep learning-based anomaly detection from ultrasonic images

Posilović, Luka; Medak, Duje; Milkovic, Fran; Subašić, Marko; Budimir, Marko; Lončarić, Sven

doi:10.1016/j.ultras.2022.106737

Cited by 21 publications

(6 citation statements)

References 18 publications

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“…Recently, machine learning algorithms have been applied to analyze ultrasonic signals [ 176 , 177 ]. In [ 178 ], ultrasonic test data were used to train six ML models predicting the degree of corrosion in reinforced concrete based on ultrasonic traits.…”

Section: Ndt Systemsmentioning

confidence: 99%

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Hassani

Dackermann

2023

Sensors

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This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.

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Section: Ndt Systemsmentioning

confidence: 99%

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Hassani

Dackermann

2023

Sensors

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“…Additionally, the area where errors occur more frequently can also be determined using the clustering method. Several clustering methods can be used for this task, including k-mean clustering [20], mini-batch k-means clustering [21], spectral clustering, gaussian mixture clustering [20], birch clustering [22], density-based clustering [23,24], hierarchical clustering, and random forest clustering [25]. All of these methods can be successfully implemented to address the two objectives (flaw/defect detection and their internal damage location).…”

Section: The Data-driven Intelligent Model For Welded Jointsmentioning

confidence: 99%

Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint

2022

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In this paper, a data-driven approach that is based on the k-mean clustering and local outlier factor (LOF) algorithm has been proposed and deployed for the management of non-destructive evaluation (NDE) in a welded joint. The k-mean clustering and LOF model algorithm, which was implemented for the classification, identification, and determination of data clusters and defect location in the welded joint datasets, were trained and validated such that three (3) different clusters and noise points were obtained. The noise points, which are regarded as the welded joint defects/flaws, allow for the determination of the cluster size, heterogeneity, and silhouette score of the welded joint data. Similarly, the LOF model algorithm was implemented for the detection, visualization, and management of flaws due to internal cracks, porosity, fusion, and penetration in the welded joint. It is believed that the management of welded joint flaws would aid the actualization of the Industry 4.0 concept in the development of lightweight products for manufacturing.

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“…With normal data as a training set, a classifier with a specific threshold is obtained and then employed to determine whether the testing images are abnormal. Currently, this technology has been already widely used in cancer detection [28], ultrasound detection [29], disease detection of industrial products [30][31][32] and infrastructure diseases [33,34]. Although anomaly detection technology can meet practical needs to some extent, there is very limited research on how to explain it.…”

Section: Introductionmentioning

confidence: 99%

A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection

Huang

Chen

2022

Applied Sciences

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Crack detection plays a pivotal role in structural health monitoring. Deep convolutional neural networks (DCNN) provide a way to achieve image classification efficiently and accurately due to their powerful image processing ability. In this paper, we propose a semi-supervised learning method based on a DCNN to achieve anomaly crack detection. In the proposed method, the training set for the network only requires a small number of normal (non-crack) images but can achieve high detection accuracy. Moreover, the trained model has strong robustness in the condition of uneven illumination and evident crack difference. The proposed method is applied to the images of walls, bridges and pavements, and the results show that the detection accuracy comes up to 99.48%, 92.31% and 97.57%, respectively. In addition, the features of the neural network can be visualized to describe its working principle. This method has great potential in practical engineering applications.

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Deep learning-based anomaly detection from ultrasonic images

Cited by 21 publications

References 18 publications

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring

Data-Driven Intelligent Model for the Classification, Identification, and Determination of Data Clusters and Defect Location in a Welded Joint

A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection

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