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Academic Journal of Computing & Information Science, 2022, 5(13); doi: 10.25236/AJCIS.2022.051307.

Sdudy on Fault Analysis Method of Optical Transmission Network Based on Deep Learning


Jiangfeng Qiang, Guiming Chen, Kai Kang, Nan Jiang

Corresponding Author:
​Jiangfeng Qiang

Rocket Force Engineering University, Xi’an, 710025, China


In order to ensure the efficient and stable operation of optical transmission network, timely and efficient fault diagnosis is very necessary. This paper studies the fault analysis method of optical transmission network based on deep learning, and summarizes the fault analysis method and research status of optical transmission network based on deep learning from two aspects of deep learning and fault diagnosis, for your reference.


Optical transmission network, Fault diagnosis, Deep learning, Artificial Intelligence

Cite This Paper

Jiangfeng Qiang, Guiming Chen, Kai Kang, Nan Jiang. Sdudy on Fault Analysis Method of Optical Transmission Network Based on Deep Learning. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 13: 44-47. https://doi.org/10.25236/AJCIS.2022.051307.


[1] Lecun Y, Bengio Y, Hinton G. Deep Learning [J]. Nature, 2015, 521(7553): 436- 444.

[2] Mcculloch W S, Pitts W. A Logical Calculus of the Ideas Immanent in Nervous Activity [J]. The Bulletin of Mathematical Biophysics, 1943, 5(4): 113-115.

[3] Minsky M, Papert S A. Perceptrons: An Introduction to Computational Geometry [M]. MIT press, 2017.

[4] Rumelhart D E, Hinton G E, Williams R J. Learning Representations by Back Propagating Errors [J]. Nature, 1986, 323 (6088): 533-536.

[5] Hinton G E, Osindero S, Teh Y W. A Fast Learning Algorithm for Deep Belief Nets [J]. Neural Computation, 2006, 18(7): 1527- 1554.

[6] Khan F N, Lu C, Lau A. Optical Performance Monitoring in Fiber-Optic Networks Enabled by Machine Learning Techniques [C] // Optical Fiber Communication Conference. 2018.

[7] Guo Yu-bin. Optical fiber Communication Technology [M]. Xi 'an: Xidian University Press, 2008. 

[8] Ji Hong-Bing, Zhou Xiao-song, Chen Hao. Research on the principle of optical path detection and its precautions [J]. China New Communications, 2017, 19(07): 120. 

[9] Liao Minmin, Chen Wei. Fault location and detection in communication optical cable lines [J]. Optical Communication Research. 2015(01): 26-27+34. 

[10] Zhang Qiongfang, Chen Genxiang, Tian Kai. Research on Online Monitoring System of Passive optical Network Based on OTDR [J]. Optical Communication Technology, 2015, 39(03): 1-3.

[11] Chen W H. Online Fault Diagnosis for Power Transmission Networks Using Fuzzy Digraph Models [J]. IEEE Transactions on Power Delivery, 2012, 27(2): 688-698.

[12] Kompella R R, Yates J, Greenberg A, et al. Fault Localization via Risk Modeling [J]. IEEE Transactions on Dependable & Secure Computing, 2009, 7(4): 396- 409.

[13] Wu B, Yeung K L. Monitoring Cycle Design for Fast Link Failure Detection in All-Optical Networks [C]// IEEE Global Telecommunications Conference. IEEE, 2009.

[14] He Hailong. Communication Cable Fault Location Technology Based on GIS [J]. Digital Communication World, 2017(06): 104-105. 

[15] Zheng Qiu-hua. Research on Key Technologies of Network Fault Intelligent Diagnosis [D]. Zhejiang University, 2007.

[16] Wang Xiang. Research on Intelligent Fault Diagnosis and Fault-tolerant Control for Complex Nonlinear Systems [D]. Yanshan University, 2012.

[17] Mas C, Tomkos I, Tonguz O K. Failure location algorithm for transparent optical networks [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(8): 1508-1519.

[18] Mhamdi L, Dhouibi H, Liouane N, et al. Multiple fault diagnosis using mathematical models [C]// 2013: 1-6.

[19] Faruk M S, Mori Y, Kikuchi K. In-Band Estimation of Optical Signal-to-Noise Ratio from Equalized Signals in Digital Coherent Receivers [J]. IEEE Photonics Journal, 2014, 6(1): 1-9.

[20] Schmogrow R, Nebendahl B, Winter M, et al. Error vector magnitude as a performance measure for advanced modulation formats [J]. IEEE Photonics Technology Letters. 2012, 24(1), 61–63.