Study of Pattern Recognition Based on Multi-Characteristic Parameters for φ-OTDR Distributed Optical Fiber Sensing System

Yan, 张颜 Zhang; Shuqin, 娄淑琴 Lou; Sheng, 梁生 Liang; Peng, 王鹏 Wang

doi:10.3788/cjl201542.1105005

Cited by 8 publications

(4 citation statements)

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“…In addition, in order to achieve the accuracy and rationality of the data information, we need to add a large number of data samples, which is not only time-consuming but also increases the cost, so we have carried out data enhancement based on time and confrontation sequences on the extracted samples, and the data Sliding slice processing, under the enhancement of adversarial examples, can also have good accurate recognition, so that more data can be generated in a small amount of high-quality data sets [2].…”

Section: Signal Data Acquisition and Processing Of Distributed Optica...mentioning

confidence: 99%

“…This paper analyzes the principle of fiber optic perimeter intrusion system, and proposes a set of system design and technical research. It uses ordinary optical [1] cable as the sensing device, based on the interference phenomenon of highly coherent back Rayleigh scattered light, takes the perimeter intrusion signal collected by the φ-OTDR [2] distributed optical fiber vibration sensing system as the research object, and completes the intelligence of the signal based on MATLAB and Python Research on recognition and classification and various machine learning algorithms. Extract the characteristics of the intrusion source signal in the time-frequency domain through discrete wavelet transform (DWT) [3] and short-time Fourier transform (STFT), and then use the CNN-LSTM [4] neural network to intelligently classify the feature signal to accurately determine the type of intrusion source , timely warning of dangerous intrusion signals.…”

Section: Introductionmentioning

confidence: 99%

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Technology research of φ-OTDR fiber perimeter intrusion system based on CNN-LSTM algorithm

Li,

Wang,

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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The fiber optic sensing perimeter security system has broad application prospects due to its unique characteristics such as long transmission distance, wide monitoring range, high sensitivity, and strong real-time performance. Based on DWT and STFT, this paper extracts the features of the intrusion source signal and uses the CNN-LSTM deep learning algorithm to intelligently classify the signal, improving the separability of disturbance events and accurately judging the intrusion source type. The average accuracy rate of classification can reach more than 96%, with excellent intelligence and low false alarm performance. This technology is a perfect replacement for traditional perimeter alarm systems such as infrared beams and electronic fences, especially applicable for real-time measurement of vibration-related events such as long-distance, omni-directional, and multi-point illegal intrusion, illegal destruction, and structural damage. In addition, the application of distributed optical fiber sensing system in the industrial chain can improve product quality, ensure production safety, and realize production line automation.This research will improve the reliability and accuracy of the perimeter security system, improve the public security environment, save social resources, and improve social security.

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Section: Signal Data Acquisition and Processing Of Distributed Optica...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Technology research of φ-OTDR fiber perimeter intrusion system based on CNN-LSTM algorithm

Li,

Wang,

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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show abstract

“…By detecting this phase change, technology enables the distributed measurement and recovery of vibration signals. 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].…”

Section: Introductionmentioning

confidence: 99%

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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“…Some methods based on signal feature extraction are proposed to recognize the event. Vries J et al [9] extracted signal features from frequency domain, Jiang et al [10] extracted the features through wavelet decomposition, Min et al [11] extracted features through Gauss mixture model, Zhu et al [12] used the level of cross rates of disturbance signal as the feature, Jiang et al [13] chose the Mel-frequency sepctrum coefficients as the features and Zhang et al [14] used multiple features for classification. These feature-based methods can achieve good recognition rate, but they need a careful selection of features and a relatively complex processing to extract these signal features.…”

Section: Introductionmentioning

confidence: 99%

An Event Recognition Method for Φ-OTDR Sensing System Based on Deep Learning

Shi

Wang

Zhao

et al. 2019

Sensors

105

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Phase-sensitive optical time domain reflectometer (Φ-OTDR) based distributed optical fiber sensing system has been widely used in many fields such as long range pipeline pre-warning, perimeter security and structure health monitoring. However, the lack of event recognition ability is always being the bottleneck of Φ-OTDR in field application. An event recognition method based on deep learning is proposed in this paper. This method directly uses the temporal-spatial data matrix from Φ-OTDR as the input of a convolutional neural network (CNN). Only a simple bandpass filtering and a gray scale transformation are needed as the pre-processing, which achieves real-time. Besides, an optimized network structure with small size, high training speed and high classification accuracy is built. Experiment results based on 5644 events samples show that this network can achieve 96.67% classification accuracy in recognition of 5 kinds of events and the retraining time is only 7 min for a new sensing setup.

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Study of Pattern Recognition Based on Multi-Characteristic Parameters for φ-OTDR Distributed Optical Fiber Sensing System

Cited by 8 publications

References 0 publications

Technology research of φ-OTDR fiber perimeter intrusion system based on CNN-LSTM algorithm

Technology research of φ-OTDR fiber perimeter intrusion system based on CNN-LSTM algorithm

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

An Event Recognition Method for Φ-OTDR Sensing System Based on Deep Learning

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