A novel fault diagnosis method for marine blower with vibration signals

• Autorzy: Yan Guohua , Hu Yihuai , Jiang Jiawei

Identyfikatory

DOI

10.2478/pomr-2022-0019

• Języki publikacji: EN

Abstrakty

EN

The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.

Słowa kluczowe

EN

fault diagnosis; marine blower; EEMD; correlation coefficient; AR spectrum; BPNN

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2022
• Tom: nr 2
• Strony: 77--86
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Yan Guohua

• Shanghai Maritime University, No.1550, Harbour Avenue, Pudong New Area, Shanghai, 201306 Shanghai, China
• Shanghai Maritime University, No.1550, Harbour Avenue, Pudong New Area, Shanghai, 201306 Shanghai, China

autor

Hu Yihuai

• Shanghai Maritime University, No.1550, Harbour Avenue, Pudong New Area, Shanghai, 201306 Shanghai, China

autor

Jiang Jiawei

• Shanghai Maritime University, No.1550, Harbour Avenue, Pudong New Area, Shanghai, 201306 Shanghai, China

Bibliografia

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

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).