{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T14:31:24Z","timestamp":1776695484533,"version":"3.51.2"},"reference-count":18,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Basic Public Welfare Research Project of Zhejiang Province","award":["LGF22E040002"],"award-info":[{"award-number":["LGF22E040002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Distributed fiber optic sensing (DFS) systems are an effective method for long-distance pipeline safety inspections. Highly accurate vibration signal identification is crucial to DFS. In this paper, we propose an end-to-end high-accuracy fiber optic vibration signal detection and identification algorithm by extracting features from the time domain and frequency domain by a one-dimensional convolutional neural network and two-dimensional convolutional neural network, respectively, and introducing a self-attentive mechanism to fuse the features of multiple modes. First, the raw signal is segmented and normalized according to the statistical characteristics of the vibration signal combined with the distribution of noise. Then, the one-dimensional sequence of vibration signal and its two-dimensional image generated by short-time Fourier transform are input to the one-dimensional convolutional neural network and two-dimensional neural network, respectively, for automatic feature extraction, and the features are combined by long and short-time memory. Finally, the multimodal features generated from the time and frequency domains are fused by a multilayer TransformerEncoder structure with a multiheaded self-attentive mechanism and fed into a multilayer perceptron for classification. Experiments were conducted on an urban field database with complex noise and achieved 98.54% accuracy, which demonstrates the effectiveness of the proposed algorithm.<\/jats:p>","DOI":"10.3390\/s22228795","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:36:40Z","timestamp":1668479800000},"page":"8795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["\u03a6-OTDR Signal Identification Method Based on Multimodal Fusion"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8294-1362","authenticated-orcid":false,"given":"Huaizhi","family":"Zhang","sequence":"first","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Ocean University, Zhoushan 316022, China"},{"name":"Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, Zhoushan 316022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6650-4998","authenticated-orcid":false,"given":"Jianfeng","family":"Gao","sequence":"additional","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Ocean University, Zhoushan 316022, China"},{"name":"Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, Zhoushan 316022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4281-3902","authenticated-orcid":false,"given":"Bingyuan","family":"Hong","sequence":"additional","affiliation":[{"name":"National-Local Joint Engineering Laboratory of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Ocean University, Zhoushan 316022, China"},{"name":"Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, Zhejiang Ocean University, Zhoushan 316022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1111\/1365-2478.12358","article-title":"Field trial of seismic recording using distributed acoustic sensing with broadside sensitive fibre-optic cables","volume":"65","author":"Hornman","year":"2017","journal-title":"Geophys. Prospect."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107082","DOI":"10.1016\/j.optlastec.2021.107082","article-title":"Optical fiber sensing for marine environment and marine structural health monitoring: A review","volume":"140","author":"Min","year":"2021","journal-title":"Opt. Laser Technol."},{"key":"ref_3","unstructured":"Qin, Z. (2013). Distributed Optical Fiber Vibration Sensor Based on Rayleigh Backscattering, University of Ottawa."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"0405010","DOI":"10.3788\/CJL201542.0405010","article-title":"Fast Pattern Recognition Based on Frequency Spectrum Analysis Used for Intrusion Alarming in Optical Fiber Fence","volume":"42","author":"Wang","year":"2015","journal-title":"Chin. J. Lasers"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1016\/j.ijleo.2016.01.165","article-title":"A new two-dimensional method to detect harmful intrusion vibrations for optical fiber pre-warning system","volume":"127","author":"Qu","year":"2016","journal-title":"Opt. Z. Licht- Elektron. = J. Light Electronoptic"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103249","DOI":"10.1016\/j.tust.2019.103249","article-title":"Leakage detection techniques for oil and gas pipelines: State-of-the-art","volume":"98","author":"Lu","year":"2020","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.jlp.2016.06.018","article-title":"Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)","volume":"43","author":"Zadkarami","year":"2016","journal-title":"J. Loss Prev. Process. Ind."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4991","DOI":"10.1109\/JLT.2019.2926745","article-title":"A Dynamic Time Sequence Recognition and Knowledge Mining Method Based on the Hidden Markov Models (HMMs) for Pipeline Safety Monitoring with \u03a6-OTDR","volume":"37","author":"Wu","year":"2019","journal-title":"J. Light. Technol."},{"key":"ref_9","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":"Zhang","year":"2018","journal-title":"Proceedings of the 2017 International Conference on Optical Instruments and Technology: Advanced Optical Sensors and Applications, Beijing, China, 28\u201330 October 2017"},{"key":"ref_10","unstructured":"Yi, S., Hao, F., Yang, A., Xin, F., and Zeng, Z. (2014, January 8\u201311). Research on wavelet analysis for pipeline pre-warning system based on phase-sensitive optical time domain reflectometry. Proceedings of the IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, Besan\u00e7on, France."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"0422003","DOI":"10.3788\/IRLA201746.0422003","article-title":"Study of pattern recognition based on SVM algorithm for \u03a6-OTDR distributed optical fiber disturbance sensing system","volume":"46","author":"Zhang","year":"2017","journal-title":"Infrared Laser Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3134","DOI":"10.1002\/mop.30886","article-title":"Pattern recognition based on enhanced multifeature parameters for vibration events in \u03a6-OTDR distributed optical fiber sensing system","volume":"59","author":"Xu","year":"2017","journal-title":"Microw. Opt. Technol. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, D., Zhang, J., Zhang, Q., and Wei, X. (2017, January 12\u201315). Classification of ECG signals based on 1D convolution neural network. Proceedings of the 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, China.","DOI":"10.1109\/HealthCom.2017.8210784"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"47565","DOI":"10.1109\/ACCESS.2021.3068292","article-title":"A Pipeline Leak Detection and Localization Approach Based on Ensemble TL1DCNN","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","unstructured":"Chao, W., Jian, W., and Zhang, X. (2017, January 5\u20139). Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network. Proceedings of the IEEE International Conference on Acoustics, New Orleans, LA, USA."},{"key":"ref_16","unstructured":"Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is All you Need. Proceedings of the NIPS, Long Beach, CA, USA."},{"key":"ref_17","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107399","DOI":"10.1016\/j.apacoust.2020.107399","article-title":"Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images","volume":"167","author":"Kumar","year":"2020","journal-title":"Appl. Acoust."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8795\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:58Z","timestamp":1760145478000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8795"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":18,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228795"],"URL":"https:\/\/doi.org\/10.3390\/s22228795","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}