A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals

1. I. Lazakis, Y. Raptodimos, T. Varelas, “Predicting ship machinery system condition through analytical reliability tools and artificial neural networks,” Ocean Engineering, vol. 152, pp. 404-415, 2018.10.1016/j.oceaneng.2017.11.017 Search in Google Scholar

2. I. Lazakis, C. Gkerekos, and G. Theotokatos, “Investigating an SVM-driven, one-class approach to estimating ship systems condition,” Ships and Offshore Structures, vol. 14, no. 5, pp. 432-441, 2019, doi: 10.1080/17445302.2018.1500189. Open DOISearch in Google Scholar

3. Y. Raptodimos and I. Lazakis, “Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications,” Ships and Offshore Structures, vol. 13, no. 6, pp. 649-656, 2018, doi: 10.1080/17445302.2018.1443694. Open DOISearch in Google Scholar

4. Y. Tan, J. Zhang, H. Tian, D. Jiang, L.Guo, G. Wang, and Y. Lin, “Multi-label classification for simultaneous fault diagnosis of marine machinery: A comparative study,” Ocean Engineering, vol. 239, p. 109723, 2021, ISSN 0029-8018, https://doi.org/10.1016/j.oceaneng.2021.109723. Search in Google Scholar

5. H. Habibi, I. Howard, and S. Simani, “Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review,” Renewable Energy, vol. 135, pp. 877-896, 2019, ISSN 0960-1481, https://doi.org/10.1016/j.renene.2018.12.066. Search in Google Scholar

6. K. Satpathi, A. Ukil, and J. Pou, “Short-Circuit Fault Management in DC Electric Ship Propulsion System: Protection Requirements, Review of Existing Technologies and Future Research Trends,” IEEE Transactions on Transportation Electrification, vol. 4, no. 1, pp. 272-291, March 2018, doi: 10.1109/TTE.2017.2788199. Open DOISearch in Google Scholar

7. É. M. Lima, C. M. dos Santos, N. S. D. Brito, B. A. de Souza, R. de Almeida Coelho, and H. Gayoso Meira Suassuna de Medeiros, “High impedance fault detection method based on the short-time Fourier transform,” IET Gener. Transm. Distrib., vol. 12, pp. 2577-2584, 2018, https://doi.org/10.1049/iet-gtd.2018.0093. Search in Google Scholar

8. K. M. Silva, B. A. Souza, and N. S. D. Brito, “Fault detection and classification in transmission lines based on wavelet transform and ANN,” IEEE Transactions on Power Delivery, vol. 21, no. 4, pp. 2058-2063, Oct. 2006, doi: 10.1109/TPWRD.2006.876659. Open DOISearch in Google Scholar

9. Y. Cheng, Z. Wang, B. Chen, W. Zhang, and G. Huang, “An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis,” ISA Transactions, vol. 91, pp. 218-234, 2019, ISSN 0019-0578, https://doi.org/10.1016/j.isatra.2019.01.038.30738582 Search in Google Scholar

10. M. E. Baran and N. R. Mahajan, “Overcurrent Protection on Voltage-Source-Converter-Based Multiterminal DC Distribution Systems,” IEEE Transactions on Power Delivery, vol. 22, no. 1, pp. 406-412, Jan. 2007, doi: 10.1109/TPWRD.2006.877086. Open DOISearch in Google Scholar

11. M. Moschopoulos, G. N. Rossopoulos, and C. I. Papadopoulos, “Journal Bearing Performance Prediction Using Machine Learning and Octave-Band Signal Analysis of Sound and Vibration Measurements,” Polish Marit. Res., vol. 28, no. 3, 2021, doi: 10.2478/pomr-2021-0041. Open DOISearch in Google Scholar

12. N. Vulić, K. Bratić, B. Lalić, and L. Stazić, “Implementing Simulationx in the Modelling of Marine Shafting Steady State Torsional Vibrations,” Polish Marit. Res., vol. 28, no. 2, 2021, doi: 10.2478/pomr-2021-0022. Open DOISearch in Google Scholar

13. Z. Wang, L. Yao, G. Chen, and J. Ding, “Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals,” ISA Transactions, vol. 114, pp. 470-484, 2021, ISSN 0019-0578, https://doi.org/10.1016/j.isatra.2020.12.054.33454055 Search in Google Scholar

14. F. Wang, “Pulsation Signals Analysis of Turbocharger Turbine Blades based on Optimal EEMD and TEO,” Polish Marit. Res., vol. 26, no. 3, 2019, doi: 10.2478/pomr-2019-0048. Open DOISearch in Google Scholar

15. H. Li, T. Liu, X. Wu, and S. Li, “Research on test bench bearing fault diagnosis of improved EEMD based on improved adaptive resonance technology,” Measurement, vol. 185, p. 109986, 2021, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2021.109986. Search in Google Scholar

16. S. M. Debbal and F. Bereksi-Reguig, “Time-frequency analysis of the first and the second heartbeat sounds,” Applied Mathematics and Computation, vol. 184, issue 2, pp. 1041-1052, 2007, ISSN 0096-3003, https://doi.org/10.1016/j.amc.2006.07.005. Search in Google Scholar

17. T. Sang, “The Self-Duality of Discrete Short-Time Fourier Transform and Its Applications,” IEEE Transactions on Signal Processing, vol. 58, no. 2, pp. 604-612, Feb. 2010, doi: 10.1109/TSP.2009.2032038. Open DOISearch in Google Scholar

18. J. Zheng and H. Pan, “Mean-optimized mode decomposition: An improved EMD approach for non-stationary signal processing,” ISA Transactions, vol. 106, pp. 392-401, 2020, ISSN 0019-0578, https://doi.org/10.1016/j.isatra.2020.06.011.32591253 Search in Google Scholar

19. R. R. Schoen, B. K. Lin, T. G. Habetler, J. H. Schlag, and S. Farag, “An unsupervised, on-line system for induction motor fault detection using stator current monitoring,” IEEE Transactions on Industry Applications, vol. 31, no. 6, pp. 1280-1286, Nov.-Dec. 1995, doi: 10.1109/28.475698. Open DOISearch in Google Scholar

20. Y. Khelil, G. Graton, M. Djeziri, M. Ouladsine, and R. Outbib, “Fault Detection and Isolation in Marine Diesel Engines: A Generic Methodology,” IFAC Proceedings, vol. 45, issue 20, pp. 964-969, 2012, ISSN 1474-6670, ISBN 9783902823090, https://doi.org/10.3182/20120829-3-MX-2028.00164. Search in Google Scholar

21. Y. Jia, G. Li, X. Dong, and K. He, “A novel denoising method for vibration signal of hob spindle based on EEMD and grey theory,” Measurement, vol. 169, p. 108490, 2021, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2020.108490. Search in Google Scholar

22. T. Berredjem and M. Benidir, “Bearing faults diagnosis using fuzzy expert system relying on an Improved Range Overlaps and Similarity method,” Expert Systems with Applications, vol. 108, pp. 134-142, 2018, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2018.04.025. Search in Google Scholar

23. H. Wang, M. Peng, J. Wesley Hines, G. Zheng, Y. Liu, and B. R. Upadhyaya, “A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants,” ISA Transactions,vol. 95, pp. 358-371, 2019, ISSN 0019-0578, https://doi.org/10.1016/j.isatra.2019.05.016.31171304 Search in Google Scholar

24. H. Qin, R. Yang, C. Guo, and W. Wang, “Fault diagnosis of electric rudder system using PSOFOA-BP neural network,” Measurement, vol. 186, p. 110058, 2021, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2021.110058. Search in Google Scholar

25. M. S. Hoseinzadeh, S. E. Khadem, and M. S. Sadooghi, “Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition,” ISA Transactions, vol. 83, pp. 261-275, 2018, ISSN 0019-0578, https://doi.org/10.1016/j.isatra.2018.09.008.30268438 Search in Google Scholar

26. G. Singh, G. Kaur, and V. Kumar, “ECG denoising using adaptive selection of IMFs through EMD and EEMD,” 2014 International Conference on Data Science & Engineering (ICDSE), 2014, pp. 228-231, doi: 10.1109/ICDSE.2014.6974643. Open DOISearch in Google Scholar

27. Z. Wang, R. Razzaghi, M. Paolone, F. Rachidi, “Time reversal applied to fault location in power networks: Pilot test results and analyses,” International Journal of Electrical Power & Energy Systems, vol. 114, p. 105382, 2020, ISSN 0142-0615, https://doi.org/10.1016/j.ijepes.2019.105382. Search in Google Scholar

28. P. Bzura, “Diagnostic Model of Crankshaft Seals,” Polish Marit. Res., vol. 26, no. 3, 2019, doi: 10.2478/pomr-2019-0044. Open DOISearch in Google Scholar

29. Z. Ye and M. K. Kim, “Predicting electricity consumption in a building using an optimized back-propagation and Levenberg–Marquardt back-propagation neural network: Case study of a shopping mall in China,” Sustainable Cities and Society, vol. 42, pp. 176-183, 2018, ISSN 2210-6707, https://doi.org/10.1016/j.scs.2018.05.050. Search in Google Scholar

30. H. K. Aggarwal, M. P. Mani, and M. Jacob, “MoDL: Model-Based Deep Learning Architecture for Inverse Problems,” IEEE Transactions on Medical Imaging, vol. 38, no. 2, pp. 394-405, Feb. 2019, doi: 10.1109/TMI.2018.2865356.676067330106719 Open DOISearch in Google Scholar

31. Z. Yang, C. Kong, Y. Wang, X. Rong, and L. Wei, “Fault diagnosis of mine asynchronous motor based on MEEMD energy entropy and ANN,” Computers & Electrical Engineering, vol. 92, p. 107070, 2021, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2021.107070. Search in Google Scholar