Bag Filter Leak Detection and Location Based on Phase-Sensitive Optical Time Domain Reflectometry

Gang, 高岗 Gao; Jun, 黎军 Li; Xu-an, 刘旭安 Liu; Bo, 史博 Shi; Yuquan, 汤玉泉 Tang; Tao, 庞涛 Pang; Qing, 郑青 Zheng; Liming, 孙礼明 Sun; Fengzhong, 董凤忠 Dong

doi:10.3788/aos201838.0706001

Cited by 3 publications

(3 citation statements)

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“…In previous studies, we used a bag airflow simulation experimental setup and a bag dust collector field experimental platform to detect and identify broken filter bags [23,24], verifying the feasibility of applying this technology to filter bag leakage detection. The recognition rates for good and broken bags were 93.8% and 97.8%, respectively.…”

Section: Introductionmentioning

confidence: 75%

“…A section of optical fiber with a length of 6 to 7 m was folded in half and secured with tape, and it was then suspended inside the filter bag being tested. The theoretical spatial resolution of the proposed sensing system is 20 m, while the actual spatial resolution measured by the experiment is 23.7 m [23]. The fiber length retained between the two filter bags must be at least 23.7 m so that the sensing system can locate each filter bag.…”

Section: Experimental Setup Principle and Signal Acquisitionmentioning

confidence: 97%

“…Thanks to the joint efforts of researchers, this technology is now widely used in various application scenarios, such as perimeter security [5][6][7][8], oil and gas pipeline detection [9][10][11], high-speed rail [12][13][14][15][16], natural hazard detection [17], geophysical prospecting [18,19], structural health monitoring [20][21][22], etc. In addition to the industries mentioned above, our research group has recently begun to use this technology in the field of bag filter damage detection, which is often used in the environmental industry [23][24][25]. At present, in the field of detection, identification, and localization of broken filter bags in bag dust collectors, traditional methods tend to directly rely on workers' personal experiences to detect damaged bags, or when the bags have been in use for some time, the worker replaces all of the filter bags in the dust collector or uses fluorescent powder to mark damaged bags.…”

Section: Introductionmentioning

confidence: 99%

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A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

Liu,

Tang,

Zhang

et al. 2024

Photonics

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The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%.

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Section: Introductionmentioning

confidence: 75%

Section: Experimental Setup Principle and Signal Acquisitionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

Liu,

Tang,

Zhang

et al. 2024

Photonics

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Pipeline Threat Event Identification Based on GAF of Distributed Fiber Optic Signals

Li,

Yao,

Zhang

et al. 2023

IEEE Sensors J.

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基于格拉姆角场的分布式光纤振动信号识别技术

Li Jun,

Yao Ruixu,

Ren Meiying

et al. 2024

中国激光

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Objective Ensuring the integrity and reliability of oil and gas pipelines is of paramount importance in safeguarding energy security and protecting the environment. However, these pipelines are often exposed to various threats, such as human sabotage and thirdparty construction excavations, which may cause severe fires and explosion accidents. Therefore, it is necessary to develop an effective method to detect and identify different types of events occurring along pipelines. In this study, distributed optical fiber vibration monitoring technology is used to collect the waveform signals of six types of events that occur along a 28.9 -kilometerlong pipeline. Subsequently, the Gramian angular field (GAF) transform is used to convert the onedimensional timeseries signals into twodimensional image information, enabling the capture of characteristic patterns for each event. Next, the GoogLeNet deep learning model is used to classify and identify image information and evaluate the recognition accuracy and false alarm rate of the model. This study proposes an efficient and accurate method for oil and gas pipeline threat identification based on the GAF transform and deep learning.Methods This study proposes a novel method for oil and gas pipeline threat identification based on distributed optical fiber vibration monitoring technology and deep learning. The waveform signals of six types of events (manual excavation, machine damage, noise, walking, vehicle damage, and water flow vibration) were collected along a 28.9 -kilometerlong pipeline, which have the potential to jeopardize pipeline safety. To enhance the feature representation of the signals, a filter was employed to remove noise, and the GAF algorithm was used to convert the onedimensional timeseries signals into twodimensional images, enabling the capture of the characteristic patterns of each event. Subsequently, three different deep learning networks, GoogLeNet, VGG, and AlexNet, were employed to classify and identify the images and compare their recognition accuracies and false alarm rates. Experiments were conducted to evaluate the performance of our method, demonstrating that GoogLeNet outperformed the other two networks in terms of recognition accuracy and detecting false alarm rates. The effect of the signaltonoise ratio (SNR) on the classification performance was analyzed. The GoogLeNet network was determined to achieve optimal classification performance when the SNR was 8 dB. Results and DiscussionsThe main contribution of this study is the proposal of a novel method for oil and gas pipeline threat identification based on the GAF algorithm and deep learning. The GAF algorithm was used to transform the waveform signals of six types of fieldcollected events (manual excavation, machine damage, noise, walking, vehicle vibration, and water flow vibration) into twodimensional images that captured the characteristic patterns of each event. Subsequently, three different deeplearning networks, GoogLeNet, VGG, and AlexNet, were used to classify and identify the imag...

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Bag Filter Leak Detection and Location Based on Phase-Sensitive Optical Time Domain Reflectometry

Cited by 3 publications

References 0 publications

A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

Pipeline Threat Event Identification Based on GAF of Distributed Fiber Optic Signals

基于格拉姆角场的分布式光纤振动信号识别技术

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