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Research on SDG Fault Diagnosis of Ocean Shipping Boiler System Based on Fuzzy Granular Computing Under Data Fusion

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1. Ciabattoni, L., Ferracuti, F., Freddi, A. and Monteriu, A.: Statistical spectral analysis for fault diagnosis of rotating machines, Ieee Transactions on Industrial Electronics, Vol. 65, no. 5, pp. 4301-4310, 2018.10.1109/TIE.2017.2762623Search in Google Scholar

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, 2018.10.1088/1361-6501/aaa33aSearch in Google Scholar

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, 2018.10.1016/j.jsv.2018.01.001Search in Google Scholar

4. Khan, S., Gani, A., Wahab, A.W.A. and Singh, P.K.: Feature selection of denial-of-service attacks using entropy and granular computing, Arabian Journal for Science and Engineering, Vol. 43, no. 2, pp. 499-508, 2018.10.1007/s13369-017-2634-8Search in Google Scholar

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, 2018.10.1016/j.ymssp.2017.12.008Search in Google Scholar

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): 2915–2930.10.1080/00207543.2013.857056Search in Google Scholar

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): 924-929.10.1109/TSMCB.2005.861879Search in Google Scholar

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.Search in Google Scholar

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, 2018.10.1016/j.knosys.2017.07.023Search in Google Scholar

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, 2018.10.1109/TIE.2017.2767540Search in Google Scholar

11. Pecht, M., Zhao, M., Kang, M. and Tang, B.: Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes, Ieee Transactions on Industrial Electronics, Vol. 65, no. 5, pp. 4290-4300, 2018.10.1109/TIE.2017.2762639Search in Google Scholar

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, 2017.10.1111/tgis.12274Search in Google Scholar

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, 2018.10.1109/TIE.2017.2767520Search in Google Scholar

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, 2018.10.1016/j.ymssp.2017.12.031Search in Google Scholar

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, 2013.10.1007/s10846-012-9793-3Search in Google Scholar

16. Wang, Q. and Gong, Z.: An application of fuzzy hypergraphs and hypergraphs in granular computing, Information Sciences, Vol. 429, no., pp. 296-314, 2018.10.1016/j.ins.2017.11.024Search in Google Scholar

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, 2018.10.3233/JCM-180786Search in Google Scholar

18. Xiahou, K.S. and Wu, Q.H.: Fault-tolerant control of doubly-fed induction generators under voltage and current sensor faults, International Journal of Electrical Power & Energy Systems, Vol. 98, no. 6, pp. 48-61, 2018.10.1016/j.ijepes.2017.11.028Search in Google Scholar

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, 2018.10.1109/TIE.2017.2758752Search in Google Scholar

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, 2018.10.1109/TPEL.2017.2713482Search in Google Scholar

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, 2018.10.1016/j.ymssp.2017.12.026Search in Google Scholar

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