References
- L. Murawski, “Identification of shaft line alignment with insufficient data availability,” Polish Maritime Research, vol. 16, pp. 35-42, 2009.
- A. Ursolov, Y. Batrak and W. Tarelko, “Application of the optimization methods to the search of marine propulsion shafting global equilibrium in running condition,” Polish Maritime Research, vol. 26, pp. 172-180, 2019.
- E. B. Donald and T. H. Charles, “Fundamentals of rotating machinery diagnostics,” American Society of Mechanical Engineers, New York, 2002.
- J. L. Perez-Ruiz, Y. Tang and I. Loboda, “Aircraft engine gas-path monitoring and diagnostics framework based on a hybrid fault recognition approach,” Aerospace, vol. 8, 2021.
- L. Bechou, L. Angrisiani, Y. Ousten, D. Dallet, H. Levi, P. Daponte, and Y. Danto, “Localization of defects in die-attach assembly by continuous wavelet transform using scanning acoustic microscopy,” Microelectronics Reliability, vol. 39, pp. 1095-1101, 1999.
- M. E. Moreno-Sánchez, J. A. Villarraga-Ossa and R. Moreno-Sánchez, “Diagnóstico de fallas tempranas de rodamientos en mecanismos susceptibles al desbalanceo y a la desalineación,” Revista UIS Ingenierías, vol. 18, pp. 187-198, 2019.
- R. G. Desavale, “Dynamics characteristics and diagnosis of a rotor-bearing’s system through a dimensional analysis approach an experimental study,” Journal of Computational and Nonlinear Dynamics, vol. 14, 2018.
- H. Talhaoui, A. Menacer, A. Kessal, and A. Tarek, “Experimental diagnosis of broken rotor bars fault in induction machine based on Hilbert and discrete wavelet transforms,” International Journal of Advanced Manufacturing Technology, vol. 95, pp. 1399-1408, 2018.
- O. C. Kalay, O. Dogan, C. Yuce, and F. Karpat, “Effects of tooth root cracks on vibration and dynamic transmission error responses of asymmetric gears: A comparative study,” Mechanics Based Design of Structures and Machines, 2023.
- J. L. Liu, Z. Gu and S. Y. Liu, “Research on MDO of ship propulsion shafting dynamics considering the coupling effect of a propeller-shafting-hull system,” Polish Maritime Research, vol. 30, pp. 86-97, 2023.
- O. Janssens, V. Slavkovikj, B. Vervisch, K. Stockman, M. Loccufier, S. Verstockt, R. Van de Walle, and S. Van Hoecke, “Convolutional neural network based fault detection for rotating machinery,” Journal of Sound and Vibration, vol. 377, pp. 331-345, 2016.
- Y. LeCun, Y. Bengio and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436-444, 2015.
- D. Hoang and H. Kang, “Rolling element bearing fault diagnosis using convolutional neural network and vibration image,” Cognitive Systems Research, vol. 53, pp. 42-50, 2019.
- K. Bousbai, J. Morales-Sanchez, M. Merah, and J. L. Sancho-Gomez, “Improving hand gestures recognition capabilities by ensembling convolutional networks,” Expert Systems, vol. 39, 2022.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR, 2016.
- M. Shafiq and Z. Q. Gu, “Deep Residual Learning For Image Recognition: A Survey,” Applied Sciences-Basel, vol. 12, 2022.
- S. Tang, S. Yuan and Y. Zhu, “Deep learning-based intelligent fault diagnosis methods toward rotating machinery,” IEEE Access, vol. 8, pp. 9335-9346, 2020.
- M. Zhao, S. Zhong, X. Fu, B. Tang, and M. Pecht, “Deep residual shrinkage networks for fault diagnosis,” IEEE Transactions on Industrial Informatics, vol. 16, pp. 4681-4690, 2020.
- M. Bach-Andersen, B. Romer-Odgaard and O. Winther, “Deep learning for automated drivetrain fault detection,” Wind Energy, vol. 21, pp. 29-41, 2018-01-01 2018.
- P. Kumar and A. S. Hati, “Transfer learning-based deep CNN model for multiple faults detection in SCIM,” Neural Computing & Applications, vol. 33, pp. 15851-15862, 2021.
- Y. X. Huangfu, E. Seddik, S. Habibi, A. Wassyng, and J. Tjong, “Fault detection and diagnosis of engine spark plugs using deep learning techniques,” SAE International Journal Of Engines, vol. 15, pp. 515-525, 2022.
- D. H. Lim and K. S. Kim, “Development of deep learning-based detection technology for vortex-induced vibration of a ship’s propeller,” Journal of Sound and Vibration, vol. 520, p. 116629, 2022.
- A. Glaeser, V. Selvaraj, S. Lee, Y. Hwang, K. Lee, N. Lee, S. Lee, and S. Min, “Applications of deep learning for fault detection in industrial cold forging,” International Journal Of Production Research, vol. 59, pp. 4826-4835, 2021.
- Z. Korczewski and K. Marszalkowski, “Energy analysis of propulsion shaft fatigue process in rotating mechanical system Part I: Testing significance of influence of shaft material fatigue excitation parameters,” Polish Maritime Research, vol. 25, pp. 211-217, 2018.
- D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, pp. 613-627, 1995.
- K. Dragomiretskiy and D. Zosso, “Variational mode decomposition,” IEEE Transactions on Signal Processing, vol. 62, pp. 531-544, 2014.
- Z. Wang and T. Oates, “Imaging time-series to improve classification and imputation,” in IJCAI, 2015