基于格拉姆角场的分布式光纤振动信号识别技术

Abstract

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...