Markov transition fields and auto-encoder-based preprocessing for event recognition of Φ-OTDR

• Autorzy: Hu Xin , Dai Jingyi Dai , Wei Ziyi , Shen Wei , Yu Hao , Wu Haiyang , Xu Yingwen , Hu Chengyong , Deng Chuanlu , Huang Yi

Identyfikatory

DOI

10.37190/oa240208

• Języki publikacji: EN

Abstrakty

EN

To improve the model training efficiency and the classification performance of the phase-sensitive optical time-domain reflectometer (Φ-OTDR) in disturbance events recognition, a preprocessing method based on Markov transition fields (MTF) and auto-encoder (AE) is proposed. The phase time series, derived from demodulation of the original scattering signals, are converted into images by using the MTF method. Subsequently, an auto-encoder is introduced to perform a dimensionality reduction characterization of the MTF images, and the outputs of the encoder will be used as features for classification. The experimental results demonstrate that, compared with directly processing time series using 1-D CNN and classifying MTF images using CNN, the features obtained by the proposed method can accelerate the training process and improve the recognition performance of the classification model. The recognition accuracy for the four classes of events on the fence reaches 95.6%, representing a 12% increase.

Słowa kluczowe

EN

distributed optical fiber sensing; Φ-OTDR; optical time-domain reflectometer; disturbance recognition; Markov transition fields; MTF; autoencoder

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2024
• Tom: Vol. 54, nr 2
• Strony: 217--229
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Hu Xin

• China State Grid Taizhou Power Supply Company, Taizhou 225300, China

autor

Dai Jingyi Dai

• Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China

autor

Wei Ziyi

• Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China

autor

Shen Wei

• China State Grid Taizhou Power Supply Company, Taizhou 225300, China

autor

Yu Hao

• China State Grid Taizhou Power Supply Company, Taizhou 225300, China

autor

Wu Haiyang

• China State Grid Taizhou Power Supply Company, Taizhou 225300, China

autor

Xu Yingwen

• China State Grid Taizhou Power Supply Company, Taizhou 225300, China

autor

Hu Chengyong

• Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China

autor

Deng Chuanlu

• Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China

autor

Huang Yi

• Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China

Bibliografia

• [1] HE Q., ZHU T., XIAO X.H., ZHANG B.M., DIAO D.M., BAO X.Y., All fiber distributed vibration sensing using modulated time-difference pulses, IEEE Photonics Technology Letters 25(20), 2013: 1955-1957. https://doi.org/10.1109/LPT.2013.2276124
• [2] PENG F., WU H., JIA X.-H., RAO Y.-J., WANG Z.-N., PENG Z.-P., Ultra-long high-sensitivity Φ-OTDR for high spatial resolution intrusion detection of pipelines, Optics Express 22(11), 2014: 13804-13810. https://doi.org/10.1364/OE.22.013804
• [3] BARRIAS A., RODRIGUEZ G., CASAS J.R., VILLALBA S., Application of distributed optical fiber sensors for the health monitoring of two real structures in Barcelona, Structure and Infrastructure Engineering 14(7), 2018: 967-985. https://doi.org/10.1080/15732479.2018.1438479
• [4] SHANG Y., YANG Y., WANG C., LIU X., WANG C., PENG G., Optical fiber distributed acoustic sensing based on the self-interference of Rayleigh backscattering, Measurement 79, 2016: 222-227. https://doi.org/10.1016/j.measurement.2015.09.042
• [5] HE X., XIE S., LIU F., CAO S., GU L., ZHENG X., ZHANG M., Multi-event waveform-retrieved distributed optical fiber acoustic sensor using dual-pulse heterodyne phase-sensitive OTDR, Optics Letters 42(3), 2017: 442-445. https://doi.org/10.1364/OL.42.000442
• [6] ZHANG X., WU J., SHAN Y., LIU Y., WANG F., ZHANG Y., Online monitoring of power transmission lines in smart grid based on distributed optical fiber sensing technology, Optoelectronic Technology 37(04), 2017: 221-229.
• [7] CATALANO E., COSCETTA A., CERRI E., CENNAMO N., ZENI L., MINARDO A., Automatic traffic monitoring by ϕ-OTDR data and Hough transform in a real-field environment, Applied Optics 60(13), 2021: 3579-3584. https://doi.org/10.1364/AO.422385
• [8] JUAREZ J.C., TAYLOR H.F., Field test of a distributed fiber-optic intrusion sensor system for long perimeters, Applied Optics 46(11), 2007: 1968-1971. https://doi.org/10.1364/AO.46.001968
• [9] KANDAMALI D.F., CAO X., TIAN M., JIN Z., DONG H., YU K., Machine learning methods for identification and classification of events in ϕ-OTDR systems: A review, Applied Optics 61(11), 2022: 2975-2997. https://doi.org/10.1364/AO.444811
• [10] RAO Y., WANG Z., WU H., RAN Z., HAN B., Recent advances in phase-sensitive optical time domain reflectometry (Ф-OTDR), Photonic Sensors 11, 2021: 1-30. https://doi.org/10.1007/ s13320-021-0619-4
• [11] XU C., GUAN J., BAO M., LU J., YE W., Pattern recognition based on enhanced multifeature parameters for vibration events in φ-OTDR distributed optical fiber sensing system, Microwave and Optical Technology Letters 59(12), 2017: 3134-3141. https://doi.org/10.1002/mop.30886
• [12] WANG X., LIU Y., LIANG S., ZHANG W., LOU S., Event identification based on random forest classifier for Φ-OTDR fiber-optic distributed disturbance sensor, Infrared Physics & Technology 97, 2019: 319-325. https://doi.org/10.1016/j.infrared.2019.01.003
• [13] XU C., GUAN J., BAO M., LU J., YE W., Pattern recognition based on time-frequency analysis and convolutional neural networks for vibrational events in φ-OTDR, Optical Engineering 57(1), 2018: 016103. https://doi.org/10.1117/1.OE.57.1.016103
• [14] WU H., CHEN J., LIU X., XIAO Y., WANG M., ZHENG Y., RAO Y., One-dimensional CNN-based intelligent recognition of vibrations in pipeline monitoring with DAS, Journal of Lightwave Technology 37(17), 2019: 4359-4366. https://doi.org/10.1109/JLT.2019.2923839
• [15] LIU M., WANG X., LIANG S., SHENG X., LOU S., Single and composite disturbance event recognition based on the DBN-GRU network in φ-OTDR, Applied Optics 62(1), 2023: 133-141. https://doi.org/ 10.1364/AO.477642
• [16] ZHAO X., SUN H., LIN B., ZHAO H., NIU Y., ZHONG X., WANG Y., ZHAO Y., MENG F., DING J., ZHANG X., DONG L., LIANG S., Markov transition fields and deep learning-based event-classification and vibration-frequency measurement for φ-OTDR, IEEE Sensors Journal 22(4), 2022: 3348-3357. https://doi.org/10.1109/JSEN.2021.3137006
• [17] JIANG J.-R., YEN C.-T., Product quality prediction for wire electrical discharge machining with Markov transition fields and convolutional long short-term memory neural networks, Applied Sciences 11(13), 2021: 5922. https://doi.org/10.3390/app11135922
• [18] GAO Z., YANG H., GONG Y., MU Y., Fault diagnosis of shunt capacitor based on Markov transfer field transformation of vibration signal, Acta Metrologica Sinica 44(9), 2023: 1339-1346.
• [19] WANG Z., OATES T., Imaging time-series to improve classification and imputation, Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015: 3939-3945.
• [20] MARKOVIN P.A., TREPAKOV V.A., GUZHVA M.E., KVYATKOVSKII O.E., RAZDOBARIN A.G., ITOH M., A crystal optical study of short range polar order in the ferroelectric phase: Doped incipient ferroelectrics, Ferroelectrics 538(1), 2019: 35-44. https://doi.org/10.1080/00150193.2019.1569983
• [21] RUMELHART D.E., HINTON G.E., WILLIAMS R.J., Learning representations by back-propagating errors, Nature 323(6088), 1986: 533-536. https://doi.org/10.1038/323533a0
• [22] BALDI P., Autoencoders, unsupervised learning, and deep architectures, [In] Proceedings of ICML Workshop on Unsupervised and Transfer Learning, Proceedings of Machine Learning Research, Vol. 27, 2012: 37-49.
• [23] THEIS L., SHI W., CUNNINGHAM A., HUSZÁR F., Lossy image compression with compressive autoencoders, Proceedings of International Conference on Learning Representations, Palais des Congrès Neptune, Toulon, France, 2017: 1-19.