A Deep Learning-Based Ultrasonic Pattern Recognition Method for Inspecting Girth Weld Cracking of Gas Pipeline

Yan, Yuchao; Liu, D.; Gao, Bin; Tian, Gui Yun; Cai, Zhichao

doi:10.1109/jsen.2020.2982680

Cited by 53 publications

(19 citation statements)

References 40 publications

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“…The detection signal is received and amplified by the receiving device to analyze the defect information [ 63 ]. Thus, this technique does not require any couplant medium and can produce a wide range of patterns [ 64 , 65 , 66 , 67 , 68 ]. It shows the potential in austenitic weld inspection [ 69 , 70 , 71 ].…”

Section: Non-destructive Testing (Ndt) For In-line Inspectionmentioning

confidence: 99%

Pipeline In-Line Inspection Method, Instrumentation and Data Management

Qiu

Tian

Zeng

et al. 2021

Sensors

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Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

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Section: Non-destructive Testing (Ndt) For In-line Inspectionmentioning

confidence: 99%

Pipeline In-Line Inspection Method, Instrumentation and Data Management

Qiu

Tian

Zeng

et al. 2021

Sensors

Self Cite

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“…Automation of the testing can generate a large amount of data to be processed. Hence machine learning and deep learning methods have been developed to conduct defect detection either on composite [1][2][3] or metallic materials [4][5][6][7][8][9]. One challenge is then to label the data before the learning stage of those methods.…”

Section: State Of the Artmentioning

confidence: 99%

“…A common approach for defect detection using learning approaches consists in the transformation of the 1D signal (A-scan) into features vector with lower dimension, using for instance wavelet transform [3,6], chirplet transform [2] or directly several convolutional layers of a neural network [7,8]. This features vector is then used at the input of a classifier.…”

Section: State Of the Artmentioning

confidence: 99%

Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

Trouvé-Peloux

Abeloos

Fekih

et al. 2021

2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation

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This paper is dedicated to out-of-plane waviness defect detection within composite materials by ultrasonic testing. We present here an in-house experimental database of ultrasonic data built on composite pieces with/without elaborated defects. Using this dataset, we have developed several defect detection methods using the C-scan representation, where the defect is clearly observable. We compare here the defect detection performance of unsupervised, classical machine learning methods and deep learning approaches. In particular, we have investigated the use of semantic segmentation networks that provides a classification of the data at the “pixel level”, hence at each C-scan measure. This technique is used to classify if a defect is detected, and to produce a precise localization of the defect within the material. The results we obtained with the various detection methods are compared, and we discuss the drawbacks and advantages of each method.

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“…The proposed model provides higher accuracy than other networks such as LSTM, GRU, or ResNet (refer to Section 2). Yan et al [40] developed a CNN-SVM (support vector machine) framework for the automated identification of pipeline girth cracking through ultrasonic signals obtained from electromagnetic acoustic transducers (EMATs). The motivation for using DL in this work is to overcome the limitations of using EMATs, i.e.…”

Section: A-scansmentioning

confidence: 99%

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

Cantero-Chinchilla,

Wilcox,

Croxford

2021

Preprint

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The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data acquisition, data pre-processing, defect detection, and defect characterisation) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data; moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve full artificial intelligence driven automation of ultrasonic NDE so that the research community, industry, and regulatory bodies embrace them. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.

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A Deep Learning-Based Ultrasonic Pattern Recognition Method for Inspecting Girth Weld Cracking of Gas Pipeline

Cited by 53 publications

References 40 publications

Pipeline In-Line Inspection Method, Instrumentation and Data Management

Pipeline In-Line Inspection Method, Instrumentation and Data Management

Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

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