Abstract
Pipeline is one of the most important transportation modes of oil and gas. However, it is different from the single-scene environments in fields such as security and high-speed railway, as the environment along the length of long-distance pipelines is complex and variable, including plains, mountains, and lakes. Different geological conditions have different effects on the transmission of vibrations, which poses great challenges to the external perception of distributed optic fiber sensing (DOFS) systems. At the same time, the similarity of different external activity signal characteristics also greatly affects the recognition performance of DOFS systems. Mechanical construction activities along the pipeline have posed a serious threat to the safety of pipelines. In this paper, an intrusion warning ensemble model based on environmental embedding is developed on the latest novel distributed optic fiber hardware system (\(\phi \)-OTDR), named Surroundings-Embedding Ensemble Learning (SEEL). Environmental embedding technology captures the fine-grained differences in spatial environment between different defense areas. At the same time, ensemble learning technology reduces the negative impact of random noise and environmental interference. A large amount of data collected in the actual environment is used for comparison experiments. The results show that this model can achieve more accurate intrusion detection accuracy and has a wider practical application range by effectively fusing environmental information. In addition, the effectiveness of the new component designed is verified through ablation experiments.
This work was supported in part by the school-enterprise cooperation project from Zhejiang Province Natural Gas Operating Co., Ltd. No. YXSCZ2020148, in part by the National Natural Science Foundation of China No. U21A20478, 62106224.
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References
Xiang, X., Shen, J., Yang, K., Zhang, G., Qian, J., Zhu, C.: Daily natural gas load forecasting based on sequence autocorrelation. In: 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 1452–1459, Beijing (2022). https://doi.org/10.1109/YAC57282.2022.10023872
Shen, J., Yang, K., Zhu, C., et al.: Third-party construction intrusion detection of natural gas pipelines based on improved YOLOv. In: 2022 Chinese Automation Congress (CAC), pp. 1844–1849, Xiamen (2022). https://doi.org/10.1109/CAC57257.2022.10054804
Yang, Y., Li, Y., Zhang, H.: Pipeline safety early warning method for distributed signal using bilinear CNN and LightGBM. In: Proceeding of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto (2021)
Zhu, C., Yang, K., Yang, Q., et al.: A comprehensive bibliometric analysis of signal processing and pattern recognition based on distributed optical fiber. Measurement 206, 112340 (2022)
Yang, Y., Zhang, H., Li, Y.: Long-distance pipeline safety early warning: a distributed optical fiber sensing semi-supervised learning method. IEEE Sens. J. 21(17), 19453–19461 (2021)
Yang, Y., Zhang, H., Li, Y.: Pipeline safety early warning by multifeature-fusion CNN and LightGBM analysis of signals from distributed optical fiber sensors. IEEE Trans. Instrum. Meas. 70(2514213), 1–13 (2021)
Yang, Y., Li, Y., Zhang, T., Zhou, Y., Zhang, H.: Early safety warnings for long-distance pipelines: a distributed optical fiber sensor machine learning approach. In: Proceeding of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), pp. 14991–14999 (2021)
Ma, F., Wang, X., Liu, X., et al.: Application of segmentation threshold method and wavelet threshold denoising based on EMD in \(\phi \)-OTDR system. In: International Conference on Information Optics and Photonics, Beijing (2018)
Xu, C., Guan, J., Bao, M., Lu, J., Ye, W.: Pattern recognition based on enhanced multifeature parameters for vibration events in \(\phi \)-OTDR distributed optical fiber sensing system. Microw. Opt. Technol. Lett. 59(12), 3134–3141 (2017)
Meng, H., Wang, S., Gao, C., Liu, F.: Research on recognition method of railway perimeter intrusions based on \(\phi \)-OTDR optical fiber sensing technology. IEEE Sens. J. 21(8), 9852–9859 (2021)
Zhu C., Yang, K., Yang, Q., Pu, Y., Jiang, H.: Visibility and meteorological parameter model based on rashomon regression analysis. In: 2022 12th International Conference on Information Science and Technology (ICIST), pp. 367–373, Kaifeng (2022). https://doi.org/10.1109/ICIST55546.2022.9926838
Yang, K., Shi, Y., Yu, Z., Yang, Q., Sangaiah, A.K., Zeng, H.: Stacked one-class broad learning system for intrusion detection in industry 4.0. IEEE Trans. Ind. Inform. 19(1), 251–260 (2023). https://doi.org/10.1109/TII.2022.3157727
Liu, G., Si, J., Meng, W., Yang, Q., Li, C.: wind turbine fault detection with multimodule feature extraction network and adaptive strategy. IEEE Trans. Instrum. Meas. 72(3504613), 1–13 (2023). https://doi.org/10.1109/TIM.2022.3227606
Lu, Y., Zhu, T., Chen, L., et al.: Distributed vibration sensor based on coherent detection of phase-OTDR. J. Lightwave Technol. 28(22), 3243–3249 (2010)
Hong, R., et al.: Enlarging dynamic strain range in UWFBG array based \(\phi \)-OTDR assisted with polarization signal. IEEE Photonics Technol. Lett. 33(18), 994–997 (2021). https://doi.org/10.1109/LPT.2021.3079186
Yang, K., Yu, Z., Chen, C.-L.-P., et al.: Incremental weighted ensemble broad learning system for imbalanced data. IEEE Trans. Knowl. Data Eng. 34(12), 5809–5824 (2022)
Yang, K., Yu, Z., Wen, X., et al.: Hybrid classifier ensemble for imbalanced data. IEEE Trans. Neural Netw. Learn. Syst. 31(4), 1387–1400 (2020). https://doi.org/10.1109/TNNLS.2019.2920246
Boom, D., Cedric, S., et al.: Representation learning for very short texts using weighted word embedding aggregation. Pattern Recogn. Lett. 80, 150–156 (2016)
Chen, T., He, T., Benesty, M., et al.: Xgboost: extreme gradient boosting. R Package Version 1(4), 1–4 (2015)
Samui, P., Kothari, D.-P.: Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Scientia Iranica 18(1), 53–58 (2011)
Wang, L., Zeng, Y., Chen, T.: Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. Appl. 42(2), 855–863 (2015)
Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena 404, 132306 (2020). https://doi.org/10.1016/j.physd.2019.132306
Yang, K., Liu, Y., Yu, Z., et al.: Extracting and composing robust features with broad learning system. IEEE Trans. Knowl. Data Eng. 35(4), 3885–3896 (2023)
Zhu, C., Pu, Y., Yang, K., et al.: Distributed optical fiber intrusion detection by image encoding and SwinT in multi-interference environment of long-distance pipeline. IEEE Trans. Instrum. Meas. 72, 1–12 (2023)
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Zhu, C., Pu, Y., Lyu, Z., Qian, J., Yang, K. (2023). Long-Distance Pipeline Intrusion Warning Based on Environment Embedding from Distributed Optical Fiber Sensing. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14263. Springer, Cham. https://doi.org/10.1007/978-3-031-44204-9_22
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