One-Dimensional CNN-Based Intelligent Recognition of Vibrations in Pipeline Monitoring With DAS

Wu, Huijuan; Chen, Jiping; Liu, Xiangrong; Xiao, Yao; Wang, Mengjiao; Zheng, Yi; Rao, Yunjiang

doi:10.1109/jlt.2019.2923839

Cited by 161 publications

(61 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Various experimental results show that CNN can effectively extract structural features for the 1-D complicated speech or sensory signals [13]. In the application of DAS, considering that the information in spatial dimension is not as rich as that in temporal dimension, an array of 1D-CNNs is then adopted to extract the structural features of each signal at all the spatial nodes in the recognition area, while we do not use a direct two-dimensional CNN (2D-CNN).…”

Section: B Temporal Feature Extraction With 1d-cnnsmentioning

confidence: 99%

“…In the application of DAS, considering that the information in spatial dimension is not as rich as that in temporal dimension, an array of 1D-CNNs is then adopted to extract the structural features of each signal at all the spatial nodes in the recognition area, while we do not use a direct two-dimensional CNN (2D-CNN). Besides, the 1D-CNN can extract the temporal feature brilliantly with fewer network parameters [13], which can improve the speed of model detection and prevent over fitting. Thus several identical 1D-CNNs are combined in parallel to form a 1D-CNN array, which is denoted as 1D-CNNs, in which each 1D-CNN is responsible for the temporal feature extraction for the signal at one spatial node.…”

Section: B Temporal Feature Extraction With 1d-cnnsmentioning

confidence: 99%

“…As stated in the related work, 1-D CNN and 2-D CNN are the frequently used deep learning networks at present for the one-dimensional DAS signal recognition at each fiber location in [13]- [16], and 1-D CNN proves better than the others including the traditional machine learning methods with fixed hand crafted feature. In this paper, we mainly compare the proposed method with the networks which can consider both the spatio-and temporal-information but not the only temporal information.…”

Section: B Feature Visualizationmentioning

confidence: 99%

“…However, intelligent recognition and deep understanding of real threats such as mechanical operations, manual diggings and other vibration sources as the traffic interferences along the fiber is still a challenging problem, which results in frequent nuisance alarms in practical applications. In fact, to improve its smart sensing ability, a lot of useful signal processing work has been involved, which includes conventional machine learning methods with fixed hand-crated feature extraction [6], [7], [11], [12], and deep learning methods with one dimensional(1-D) [13] or two dimensional (2-D) Convolution Neural Networks (CNNs) [14]- [16] to extract the hidden distinguishable feature automatically.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

Yang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Extracting more and more accurate information to understand the detected vibration or acoustic targets better, has always been an important goal in signal recognition for Distributed Acoustic Sensor(DAS) with optical fiber. In this paper, we use one-dimensional Convolution Neural Networks(1D-CNNs) to extract the detailed temporal structure information at each signal node and utilize a bidirectional Long Short Term Memory(BiLSTM) network to dig out the spatial relationship among the different signal nodes, and then propose a novel identification method by treating the spatial-and temporalinformation in a different way, which is denoted as the 1DCNNs-BiLSTM model. The experimental results on the field data show better recognition performance can be achieved in the safety monitoring of the buried optical communication cable in urban with DAS. It helps to improve the recognition rate further compared with the other deep-learning methods frequently or possibly used for DAS signal recognition, such as the 1D-CNNs with a single temporal feature extraction, and 1DCNN-CNN and 2D-CNN models with simultaneous spatiotemporal feature learning. To the best of our knowledge, it is the first time to simultaneously extract and utilize the detailed temporal structure feature and the overall spatial connection through a customized deep learning network.

show abstract

Section: B Temporal Feature Extraction With 1d-cnnsmentioning

confidence: 99%

Section: B Temporal Feature Extraction With 1d-cnnsmentioning

confidence: 99%

Section: B Feature Visualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

Yang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, convolutional neural networks (CNN) based deep-learning is quite suitable for Φ-OTDR event classification. Some researches focused on event recognition in distributed optical fiber sensors using neural networks have been reported [17,18,19,20,21,22]. M. Aktas et al [17] and L. Shiloh et al [18] reported the high potential of deep learning in data processing for Φ-OTDR system in 2017 and 2018.…”

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

View full text Add to dashboard Cite

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.

show abstract

Event-Detection Deep Neural Network for OTDR Trace Analysis

Rutigliano

Boracchi

Invernizzi

et al. 2021

Proceedings of the International Neural Networks Society

View full text Add to dashboard Cite

The Optical Time Domain Reflectometer (OTDR) is an optoelectronic instrument used to characterize an optical fiber using the measure of scattered or reflected light from points along the fiber. The resulting signal, namely the OTDR trace, is commonly used to identify and localize possible critical events in the fiber. In this work we address the problem of automatically detecting optical events in OTDR traces, and present the first 1D object-detection neural network for optical trace analysis. Our approach takes inspiration from a successful object detection network in images, the Faster R-CNN, which we adapt to time series domain. The proposed network can both classify and localize many optical events along an input trace. Our results show that the proposed solution is more accurate than existing software currently analyzing OTDR traces, improving the mean average precision score by 27.43%. In contrast with existing solutions that are not able to distinguish many types of events, our algorithm can be trained in an end-to-end manner to detect potentially any type of optic event. Moreover, our network has been deployed on embedded OTDR devices to be executed in real-time.

show abstract

One-Dimensional CNN-Based Intelligent Recognition of Vibrations in Pipeline Monitoring With DAS

Cited by 161 publications

References 16 publications

A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

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

Event-Detection Deep Neural Network for OTDR Trace Analysis

Contact Info

Product

Resources

About