Potential fault region detection in TFDS images based on convolutional neural network

Sun, Junhua; Xiao, Zhongwen

doi:10.1117/12.2246551

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Several real-time bolt looseness evaluation methods were recommended. 98,99 Sun et al 100 suggested discovering bolt-loosened flange connections of a steel structure . To solve concerns of difficulty in identifying bolt image state collected from any arbitrary point of view and a high-performance bolt loose identification model, Nguyen et al 101 presented a novel vision-based bolt open detection technique .…”

Section: Detection Methods For Visual/biological Leakagementioning

confidence: 99%

A comprehensive study of techniques utilized for structural health monitoring of oil and gas pipelines

Sharma,

Tewari,

Biswas

et al. 2023

Structural Health Monitoring

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An extensive network of pipelines is extensively employed worldwide to carry oil and gas fluids over millions of kilometers. The pipeline channel was constructed to resist environmental dynamic loading conditions to provide safe and reliable oil and gas fluids transportation from the production well sites to depot stations installed at sea coastlines. However, pipeline infrastructure damages such as fractures, cracks, leakages, etc., are significant sources of economic losses in pipeline operations. Moreover, pipeline failures can cause considerable ecological catastrophes, human deaths, and financial loss. Important research initiatives have been committed to establishing pipeline breach detection and localization using various techniques to avoid these threats and maintain a secure and dependable pipeline network. This paper reviews different state-of-the-art damage detection methods and their recent advancement with a case study explaining the application of light detection and ranging for pipeline damage detection. The pros and cons of diverse damage detection methods in pipeline networks are also discussed. Research gaps for pipeline damage detection systems are also provided for better understanding and future research.

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Section: Detection Methods For Visual/biological Leakagementioning

confidence: 99%

A comprehensive study of techniques utilized for structural health monitoring of oil and gas pipelines

Sharma,

Tewari,

Biswas

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…As bolt connections are widely utilised in the assembly of different sections of petroleum pipeline systems, effective technique for monitoring bolted flange connections is essential. Several vision-based assessment methods for real-time bolt looseness detection have been proposed [122,123,124]. Nguyen et al [125] proposed a vision-based algorithm to identify bolt-looseness in steel structure bolted flange connections.…”

Section: Visual/ Biological Leak Detection Methodsmentioning

confidence: 99%

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Adegboye

Fung

Karnik

2019

Sensors

295

144

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Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

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“…Depending on different application situation or data types, an anomaly is also known as outlier or novelty [3]. Approaches and applications of anomaly detection exist in various domains, such as fraud detection [4], video surveillance [5,6], health-care [7], security check [8], fault detection [9,10], and defect detection [11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection

2022

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This paper proposed a novel anomaly detection (AD) approach of high-speed train images based on convolutional neural networks and the Vision Transformer. Different from previous AD works, in which anomalies are identified with a single image using classification, segmentation, or object detection methods, the proposed method detects abnormal difference between two images taken at different times of the same region. In other words, we cast anomaly detection problem with a single image into a difference detection problem with two images. The core idea of the proposed method is that the “anomaly” commonly represents an abnormal state instead of a specific object, and this state should be identified by a pair of images. In addition, we introduced a deep feature difference AD network (AnoDFDNet) which sufficiently explored the potential of the Vision Transformer and convolutional neural networks. To verify the effectiveness of the proposed AnoDFDNet, we gathered three datasets, a difference dataset (Diff dataset), a foreign body dataset (FB dataset), and an oil leakage dataset (OL dataset). Experimental results on the above datasets demonstrate the superiority of the proposed method. In terms of the F1-score, the AnoDFDNet obtained 76.24%, 81.04%, and 83.92% on Diff dataset, FB dataset, and OL dataset, respectively.

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Potential fault region detection in TFDS images based on convolutional neural network

Cited by 8 publications

References 4 publications

A comprehensive study of techniques utilized for structural health monitoring of oil and gas pipelines

A comprehensive study of techniques utilized for structural health monitoring of oil and gas pipelines

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection

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