{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:09:09Z","timestamp":1774922949699,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,8,4]],"date-time":"2019-08-04T00:00:00Z","timestamp":1564876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Youth Foundation of China","award":["61801283"],"award-info":[{"award-number":["61801283"]}]},{"name":"Scientific Research Foundation of Shantou University","award":["NTF18007"],"award-info":[{"award-number":["NTF18007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Phase-sensitive optical time domain reflectometer (\u03a6-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 \u03a6-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 \u03a6-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.<\/jats:p>","DOI":"10.3390\/s19153421","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T03:25:22Z","timestamp":1564975522000},"page":"3421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":139,"title":["An Event Recognition Method for \u03a6-OTDR Sensing System Based on Deep Learning"],"prefix":"10.3390","volume":"19","author":[{"given":"Yi","family":"Shi","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, School of Engineering, Shantou University, Shantou 515063, China"}]},{"given":"Yuanye","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, School of Engineering, Shantou University, Shantou 515063, China"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, School of Engineering, Shantou University, Shantou 515063, China"}]},{"given":"Zhun","family":"Fan","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, School of Engineering, Shantou University, Shantou 515063, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1109\/JLT.2005.849924","article-title":"Distributed Fiber-Optic Intrusion Sensor System","volume":"23","author":"Juarez","year":"2005","journal-title":"J. Lightwave Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4445","DOI":"10.1109\/JLT.2016.2542981","article-title":"Towards Prevention of Pipeline Integrity Threats using a Smart Fiber Optic Surveillance System","volume":"34","author":"Tejedor","year":"2016","journal-title":"J. Lightwave Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1364\/AO.46.001968","article-title":"Field test of a distributed fiber-optic intrusion sensor system for long perimeters","volume":"46","author":"Juarez","year":"2007","journal-title":"Appl. Opt."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.sna.2018.02.033","article-title":"On-line monitoring system of 35 kV 3-core submarine power cable based on \u03c6-OTDR","volume":"273","author":"Lv","year":"2018","journal-title":"Sens. Actuators A Phys."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fu, Y., Wang, Z., Zhu, R., Xue, N., Jiang, J., Lu, C., Zhang, B., Yang, L., Atubga, D., and Rao, Y. (2018). Ultra-Long-Distance Hybrid BOTDA\/\u03a6-OTDR. Sensors, 18.","DOI":"10.3390\/s18040976"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1109\/TPWRD.2019.2892306","article-title":"A Non-Intrusive Electrical Discharge Localization Method for Gas Insulated Line Based on Phase-Sensitive OTDR and Michelson Interferometer","volume":"34","author":"Ma","year":"2019","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10573","DOI":"10.1364\/OE.26.010573","article-title":"Wavelength-scanning coherent OTDR for dynamic high strain resolution sensing","volume":"26","author":"Liehr","year":"2018","journal-title":"Opt. Express"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/JLT.2017.2767086","article-title":"Frequency Response Enhancement of Direct-detection Phase-Sensitive OTDR by using Frequency Division Multiplexing","volume":"36","author":"Yang","year":"2018","journal-title":"J. Lightwave Technol."},{"key":"ref_9","unstructured":"Vries, J. (2004, January 15\u201317). A low-cost fence impact classification system with neural networks. Proceedings of the 7th AFRICON Conferencein Africa, Gaborone, Botswana."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jiang, L.H., Liu, X.M., and Zhang, F. (2010, January 11\u201314). Multi-target recognition used in airpoty fiber fence warning system. Proceedings of the 2010 International Conference on Machine Learning and Cybernetics, Qingdao, China.","DOI":"10.1109\/ICMLC.2010.5580929"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Min, H.K., Lee, C.Y., Lee, J.S., and Park, C.H. (2007, January 5\u20138). Abnormal signal detection in gas pipes using neural networks. Proceedings of the 33rd Annual Conference of the IEEE Industrial Electronics Society, Taipei, Taiwan.","DOI":"10.1109\/IECON.2007.4460266"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhu, H., Pan, C., and Sun, X. (2014, January 9). Vibration pattern recognition and classification in OTDR based distributed optical-fiber vibration sensing system. Proceedings of the Smart Sensor Phenomena, Technology, Networks, and Systems Integration 2014, San Diego, CA, USA.","DOI":"10.1117\/12.2045268"},{"key":"ref_13","first-page":"134","article-title":"Study of pattern recognition based on multicharacteristic parameters for \u03c6-OTDR distributed optical fiber sensing system","volume":"42","author":"Zhang","year":"2015","journal-title":"Chin. J. Lasers"},{"key":"ref_14","first-page":"1061804","article-title":"An event recognition method for fiber distributed acoustic sensing systems based on the combination of MFCC and CNN","volume":"Volume 10618","author":"Jiang","year":"2018","journal-title":"Proceedings of the International Conference on Optical Instruments and Technology: Advanced Optical Sensors and Applications"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15179","DOI":"10.3390\/s150715179","article-title":"Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction","volume":"15","author":"Sun","year":"2015","journal-title":"Sensors"},{"key":"ref_16","unstructured":"Zhang, P. (2018). Graphic Deep Learning and Neural Network, Electronic Industry Press."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1364\/OE.27.007405","article-title":"Real-time dynamic strain sensing in optical fibers using artificial neural networks","volume":"27","author":"Liehr","year":"2019","journal-title":"Opt. Express"},{"key":"ref_18","first-page":"102080","article-title":"Deep learning based multi-threat classification for phase-OTDR fiber optic distributed acoustic sensing applications","volume":"10208","author":"Aktas","year":"2017","journal-title":"SPIE Commer. Sci. Sens. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, H., Chen, J., Liu, X., Xiao, Y., Wang, M., Zheng, Y., and Rao, Y.J. (2019). 1-D CNN based intelligent recognition of vibrations in pipeline monitoring with DAS. J. Lightwave Technol., 37.","DOI":"10.1109\/JLT.2019.2923839"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shiloh, L., Eyal, A., and Giryes, R. (2018, January 24\u201328). Deep learning approach for processing fiber-optic DAS seismic data. Proceedings of the 26th International Conference on Optical Fiber Sensors (Optical Society of America, 2018), Lausanne, Switzerland.","DOI":"10.1364\/OFS.2018.ThE22"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tejedor, J., Macias-Guarasa, J., Martins, H.F., Pastor-Graells, J., Corredera, P., and Martin-Lopez, S. (2017). Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review. Appl. Sci., 7.","DOI":"10.3390\/app7080841"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, H., Zhao, C., Liao, R., Chang, Y., and Tang, M. (2018, January 24\u201328). Performance enhancement of ROTDR using deep convolutional neural networks. Proceedings of the 26th International Conference on Optical Fiber Sensors (Optical Society of America, 2018), Lausanne, Switzerland.","DOI":"10.1364\/OFS.2018.TuE16"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_24","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yazan, E., and Talu, M.F. (2017, January 16\u201317). Comparison of the stochastic gradient descent based optimization techniques. Proceedings of the 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkey.","DOI":"10.1109\/IDAP.2017.8090299"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/15\/3421\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:13:17Z","timestamp":1760188397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/15\/3421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,4]]},"references-count":29,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["s19153421"],"URL":"https:\/\/doi.org\/10.3390\/s19153421","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,4]]}}}