Deep-Learning-Based Pipe Leak Detection Using Image-Based Leak Features

Bae, Ji‐Hoon; Yeo, Doyeob; Yoon, Doo-Byung; Oh, Se Won; Kim, Gwan Joong; Kim, Nae-Soo; Pyo, Cheol Sig

doi:10.1109/icip.2018.8451489

Cited by 17 publications

(7 citation statements)

References 21 publications

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“…Stator winding [42] Injection molding [43] Rolling element bearing [44] MLP [42] CNN [43], [44] Control systems [42]- [44] Productivity [42], [43] Stability [42], [44] Fault Detection Nuclear power plant [45], [46] Electric arc system [47] Wastewater treatment [48] Oil&Gas pipeline [49], [50] DNN [45] CNN [46], [47], [49] Deep Clustering [48] LSTM [49], [50] SVM [50] Control systems [45] Infrastructures [46]- [50] Stability [45]- [50] Fault Prediction…”

Section: Defect Detectionmentioning

confidence: 99%

Section: Defect Detectionmentioning

confidence: 99%

“…Researchers in [46], [49], [50] propose various methods for pipeline leak detection. In [46], they implement a CNNbased approach using trajectory-based image features derived from time-series acoustic data. Similarly, in [49], effective techniques are presented, including 2D CNN and LSTM-AE, which convert time-series data into spectrograms for improved accuracy.…”

Section: Defect Detectionmentioning

confidence: 99%

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A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-Augmented ICPS

Chae,

Lee,

Jang

et al. 2023

Trans. Ind. Cyb-Phy. Sys.

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Digital Transformation integrates information technology across a broad spectrum of industrial sectors. Industrial Cyber-Physical Systems (ICPS) play a vital role in this transformation by harmonizing machinery, production, logistics, and societal needs through innovative information technologies. This paper investigates the adoption of industrial artificial intelligence (industrial AI) as a methodology for effective ICPS design, introducing AI-Augmented ICPS (AICPS). The study conducts a survey, focusing on the components and interactions of AICPS. We propose design considerations for the implementation of AICPS and investigate the application of cutting-edge industrial AI techniques in each interaction. From the standpoint of AI augmentation, this paper offers insights by identifying key perspectives, including uncertainty of information, safety of AI, explainability of AI, human-societal interactive ICPS, and standardization of industrial AI. This paper aims to enhance understanding of AICPS and lay the groundwork for integrating independent industrial AI techniques into ICPS.

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Section: Defect Detectionmentioning

confidence: 99%

Section: Defect Detectionmentioning

confidence: 99%

Section: Defect Detectionmentioning

confidence: 99%

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A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-Augmented ICPS

Chae,

Lee,

Jang

et al. 2023

Trans. Ind. Cyb-Phy. Sys.

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“…In the past few years, as deep-learning technology has advanced dramatically, state-ofthe-art deep neural network (DNN) models find applications in several fields, ranging from computer vision to natural language processing [1][2][3][4][5][6][7][8][9][10]. Modern DNNs are based on the convolutional neural network (CNN) structure [11], such as AlexNet [12], GoogleNet [13], VGGNet [14], the residual network (ResNet) [15,16], a densely connected convolutional network (DenseNet) [17], and EfficientNet [18], that has achieved increased accuracy by expanding more layers.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Optimization-Based Knowledge Transfer of Layer-Wise Dense Flow for Image Classification

2021

Self Cite

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A deep-learning technology for knowledge transfer is necessary to advance and optimize efficient knowledge distillation. Here, we aim to develop a new adversarial optimization-based knowledge transfer method involved with a layer-wise dense flow that is distilled from a pre-trained deep neural network (DNN). Knowledge distillation transferred to another target DNN based on adversarial loss functions has multiple flow-based knowledge items that are densely extracted by overlapping them from a pre-trained DNN to enhance the existing knowledge. We propose a semi-supervised learning-based knowledge transfer with multiple items of dense flow-based knowledge extracted from the pre-trained DNN. The proposed loss function would comprise a supervised cross-entropy loss for a typical classification, an adversarial training loss for the target DNN and discriminators, and Euclidean distance-based loss in terms of dense flow. For both pre-trained and target DNNs considered in this study, we adopt a residual network (ResNet) architecture. We propose methods of (1) the adversarial-based knowledge optimization, (2) the extended and flow-based knowledge transfer scheme, and (3) the combined layer-wise dense flow in an adversarial network. The results show that it provides higher accuracy performance in the improved target ResNet compared to the prior knowledge transfer methods.

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“…The processed byte‐type data of the wireless sensor are displayed—as either “ON” or “OFF”—on the LED matrix element; the long‐ranged camera captures this LED panel; the server restores the LED matrix image data transmitted from the smart sensor module to digital data through deep‐learning‐based individual LED elements classification and image preprocessing. With the considerable development of deep‐learning technology, several related industries have applied various deep‐learning techniques in various fields to prevent potential worksite hazards 7–24 . This study performs the ON/OFF classification that applies a deep‐learning technique to each LED element after individually dividing preprocessed LED matrix image data.…”

Section: Introductionmentioning

confidence: 99%

Deep‐learning‐based nuclear power plant fault detection using remote light‐emitting diode array data transmission

Choi

Bae

Yeo

et al. 2021

Micro & Optical Tech Letters

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This paper proposes a deep‐learning‐based wireless sensor system that uses an embedded two‐dimensional (2D) light‐emitting diode (LED) array to display measured sensor data and remote data transmission to detect nuclear power plant (NPP) equipment defects. The frequent use of electromagnetic waves often interferes with the operation of NPP. Therefore, we devised a wireless image transmission network using a 2D LED array panel that includes a sensor module and a camera to capture LED array images. Based on the experimental results, the proposed method adopting deep‐learning‐based LED array data extraction produces reliable digital data restoration performance in terms of classification accuracy, even in a complex noise environment.

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Deep-Learning-Based Pipe Leak Detection Using Image-Based Leak Features

Cited by 17 publications

References 21 publications

A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-Augmented ICPS

A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-Augmented ICPS

Adversarial Optimization-Based Knowledge Transfer of Layer-Wise Dense Flow for Image Classification

Deep‐learning‐based nuclear power plant fault detection using remote light‐emitting diode array data transmission

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