Research on SDG fault diagnosis of ocean shipping boiler system based on fuzzy granular computing under data fusion

• Autorzy: Zhu Y. , Geng L.

Identyfikatory

DOI

10.2478/pomr-2018-0079

• Języki publikacji: EN

Abstrakty

EN

The research work in this paper belongs to the application of granular computing, graph theory and its application in fault detection and diagnosis. It is a cross cutting and frontier research field in computer science, information science and graph theory. The results of this paper are of great significance to the application of the fault detection and diagnosis of the ocean boilers system. This research combines granular computing theory and signed directed graph, and proposes a new method of fault diagnosis, and applies it to the fault diagnosis of ocean ship boiler system.

Słowa kluczowe

EN

granular computing; symbolic digraph; fault diagnosis; fuzzy theory

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2018
• Tom: S 2
• Strony: 92--97
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Zhu Y.

• School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
• School of Science, Hubei University of Technology, Wuhan 430068, China

autor

Geng L.

• School of Science, Hubei University of Technology, Wuhan 430068, China

Bibliografia

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• 2. He, W., He, Y., Luo, Q. and Zhang, C.: Fault diagnosis for analog circuits utilizing time-frequency features and improved vvrkfa, Measurement Science and Technology, Vol. 29, no. 4, pp. 1-4, 2018.
• 3. Jack, Q., John, E. and Pan, Y.: Multi-scale stochastic resonance spectrogram for fault diagnosis of rolling element bearings, Journal of Sound and Vibration, Vol. 420, no. 2, pp. 174-184, 2018.
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• 5. Li, Y., Li, G., Yang, Y., Liang, X. and Xu, M.: A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy, Mechanical Systems and Signal Processing, Vol. 105, no. 4, pp. 319-337, 2018.
• 6. Wang, Y., Zheng, Y., Fang, H.-J., Wang, Y.-W.: ARMAX model based run-to-run fault diagnosis approach for batch manufacturing process with metrology delay. International Journal of Production Research, 2014, 52(10): 2915–2930.
• 7. Zheng,Y., Fang,H.-J., Wang,H.-O.: Takagi-Sugeno fuzzy model-based fault detection for networked control systems with markov delays. IEEE Transactions on System, Man and Cybernetics, Part B: Cybernetics, 2006, 36(3): 924-929.
• 8. Liu, H., Li, J., Guo, H. and Liu, C.: Interval analysis-based hyperbox granular computing classification algorithms, Iranian Journal of Fuzzy Systems, Vol. 14, no. 5, pp. 139-156, 2017.
• 9. Lung, J., Chen, Q., Mao, N. and Jack, P.: Combining granular computing technique with deep learning for service planning under social manufacturing contexts, Knowledge-Based Systems, Vol. 143, no., pp. 295-306, 2018.
• 10. Micheal, J., Zi, Y., Chen, J., Zhou, Z. and Wang, B.: Liftingnet: A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification, Ieee Transactions on Industrial Electronics, Vol. 65, no. 6, pp. 4973-4982, 2018.
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• 12. Sheikhian, H., Delavar, M.R. and Stein, A.: A gis-based multi-criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks, Transactions in Gis, Vol. 21, no. 6, pp. 1237-1259, 2017.
• 13. Wang, J., Cheng, F., Qiao, W. and Qu, L.: Multiscale filtering reconstruction for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions, Ieee Transactions on Industrial Electronics, Vol. 65, no. 5, pp. 4268-4278, 2018.
• 14. Wang, L., Liu, Z., Miao, Q. and Zhang, X.: Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings, Mechanical Systems and Signal Processing, Vol. 106, no. 5, pp. 24-39, 2018.
• 15. Wang, M., Hu, N.-Q. and Qin, G.-J.: A method for rule extraction based on granular computing: Application in the fault diagnosis of a helicopter transmission system, Journal of Intelligent & Robotic Systems, Vol. 71, no. 3-4, pp. 445-455, 2013.
• 16. Wang, Q. and Gong, Z.: An application of fuzzy hypergraphs and hypergraphs in granular computing, Information Sciences, Vol. 429, no., pp. 296-314, 2018.
• 17. Wu, H., Liu, Y., Yan, P., Fang, G. and Zhong, J.: A frequent itemset mining algorithm based on composite granular computing, Journal of Computational Methods in Sciences and Engineering, Vol. 18, no. 1, pp. 247-257, 2018.
• 18. Xiahou, K.S. and Wu, Q.H.: Fault-tolerant control of doublyfed induction generators under voltage and current sensor faults, International Journal of Electrical Power & Energy Systems, Vol. 98, no. 6, pp. 48-61, 2018.
• 19. Yang, S.-C., Hsu, Y.-L., Chou, P.-H., Chen, G.-R. and Jian, D.-R.: Online open-phase fault detection for permanent magnet machines with low fault harmonic magnitudes, Ieee Transactions on Industrial Electronics, Vol. 65, no. 5, pp. 4039-4050, 2018.
• 20. Yu, Y., Zhao, Y., Wang, B., Huang, X. and Xu, D.: Current sensor fault diagnosis and tolerant control for vsi-based induction motor drives, Ieee Transactions on Power Electronics, Vol. 33, no. 5, pp. 4238-4248, 2018.
• 21. Zheng, B., Li, Y.-F. and Huang, H.-Z.: Intelligent fault recognition strategy based on adaptive optimized multiple centers, Mechanical Systems and Signal Processing, Vol. 106, no. 7, pp. 526-536, 2018.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).