Research on big data attribute selection method in submarine optical fiber network fault diagnosis database

• Autorzy: Chen G.
• Języki publikacji: EN

Abstrakty

EN

At present, in the fault diagnosis database of submarine optical fiber network, the attribute selection of large data is completed by detecting the attributes of the data, the accuracy of large data attribute selection cannot be guaranteed. In this paper, a large data attribute selection method based on support vector machines (SVM) for fault diagnosis database of submarine optical fiber network is proposed. Mining large data in the database of optical fiber network fault diagnosis, and calculate its attribute weight, attribute classification is completed according to attribute weight, so as to complete attribute selection of large data. Experimental results prove that ,the proposed method can improve the accuracy of large data attribute selection in fault diagnosis database of submarine optical fiber network, and has high use value

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2017
• Tom: 24
• Numer: Special Issue S3
• Opis fizyczny: p.221-227,fig.,ref.

Twórcy

autor

Chen G.

• School of Software, South China Normal University, Foshan, 528225, China

Bibliografia

• 1. Karabadji N E I, Seridi H, Khelf I, et al. Improved decision tree construction based on attribute selection and data sampling for fault diagnosis in rotating machines. Engineering Applications of Artificial Intelligence, 2014, 35(35):71-83.
• 2. Zhang Q H, Qin A, Shu L, et al. Vibration sensor based intelligent fault diagnosis system for large machine unit in petrochemical industry. International Journal of Distributed Sensor Networks, 2015, 2015(3):1376-1381.
• 3. Jin S, Cui W, Jin Z, et al. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection.. Sensors, 2015, 15(7):17366-17396.
• 4. Panda M, Khilar P M. Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test. Ad Hoc Networks, 2015, 25(PA):170-184.
• 5. Zhang Q H, Hu Q, Sun G, et al. Concurrent Fault Diagnosis for Rotating Machinery Based on Vibration Sensors. International Journal of Distributed Sensor Networks, 2015, 2013(1):59-72.
• 6. Reppa V, Polycarpou M M, Panayiotou C G. Distributed Sensor Fault Diagnosis for a Network of Interconnected Cyberphysical Systems. IEEE Transactions on Control of Network Systems, 2015, 2(1):11-23.
• 7. Islam R, Khan S A, Kim J M. Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors. Journal of Sensors, 2016, 2016(2):1-16.
• 8. LAn-qiang, Liu Z, Yin C Q, et al. A Fault Diagnosis Method Forwavelet Packet and Neural Network-Based Submarine Cables. Study on Optical Communications, 2016, 42(2):16-22..
• 9. Gao Y, Yang C, Tian S, et al. Entropy Based Test Point Evaluation and Selection Method for Analog Circuit Fault Diagnosis. Mathematical Problems in Engineering, 2014, 2014(6):1-16.
• 10. Lei Y, Jia F, Lin J, et al. An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data. IEEE Transactions on Industrial Electronics, 2016, 63(5):3137-3147.
• 11. Wang S, Sun X, Li C. Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold. Mathematical Problems in Engineering, 2015, 2014(4):1-10.
• 12. Jin X, Chow T W S, Sun Y, et al. Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis. Wireless Networks, 2015, 21(3):829-839.
• 13. Kelkar S, Kamal R. Adaptive Fault Diagnosis Algorithm for Controller Area Network. IEEE Transactions on Industrial Electronics, 2014, 61(10):5527-5537.
• 14. Unal M, Onat M, Demetgul M, et al. Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement, 2014, 58:187–196.
• 15. Lu Chong, Xu Hui, Yang Yongchun. Research and application of . decision tree classification algorithm based on electronic design engineering, 2016, 24 (18): 1-3.
• 16. Gao, W. and W. Wang, The fifth geometric-arithmetic index of bridge graph and carbon nanocones. Journal of Difference Equations and Applications, 2017. 23(1-2SI): p. 100-109.
• 17. Gao, W., et al., Distance learning techniques for ontology similarity measuring and ontology mapping. Cluster Computing-The Journal of Networks Software Tools and Applications, 2017. 20(2SI): p. 959-968.
• 18. Xue C, Jing L I, Wang H, et al. Effects of Target and Distractor Saturations on the Cognitive Performance of an Integrated Display Interface. Chinese Journal of Mechanical Engineering, 2015, 28(1):208-216.

Identyfikatory

DOI

10.1515/pomr-2017-0126