Accès libre

Journal Bearing Performance Prediction Using Machine Learning and Octave-Band Signal Analysis of Sound and Vibration Measurements

À propos de cet article

Citez

1. K. Saridakis, P. Nikolakopoulos, C. Papadopoulos, A. Dentsoras, “Fault Diagnosis of Journal Bearings Based on Artificial Neural Networks and Measurements of Bearing Performance Characteristics,” in Ninth International Conference on Computational Structures Technology, Stirlingshire, 2008. Search in Google Scholar

2. N. T. Babu, A. Aravind, A. Rakesh, M. Jahzan, “Automatic Fault Classification for Journal Bearings Using ANN and DNN,” Archives of Acoustics, vol. 43, pp. 727-738, 2018. Search in Google Scholar

3. S. Y. Wang, D. X. Yang, H. F. Hu, “Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring”, Sensors (Basel, Switzerland), vol. 18(4), 1111, 2018.10.3390/s18041111594889329621175 Search in Google Scholar

4. S. Poddar, N. Tandon, “Detection of particle contamination in journal bearing using acoustic emission and vibration monitoring techniques,” Tribology International, vol. 134, pp. 154-164, 2019.10.1016/j.triboint.2019.01.050 Search in Google Scholar

5. G. N. Rossopoulos, C. I. Papadopoulos, C. Leontopoulos, “Tribological comparison of an optimum single and double slope design of the stern tube bearing, case study for a marine vessel”, Tribology International, vol. 150, ID 106343, 2020.10.1016/j.triboint.2020.106343 Search in Google Scholar

6. Y. Batrak, R. Batrak, D. Berin, A. Mikhno. “Propulsion shafting whirling vibration: case studies and perspective”, in SNAME 14th Propeller and Shafting Symposium, OnePetro, 2015.10.5957/PSS-2015-002 Search in Google Scholar

7. Elastic Shaft Alignment (ESA), Bureau Veritas, Neuilly-sur-Seine, 2015. Search in Google Scholar

8. Guide for Enhanced Shaft Alignment, American Bureau of Shipping, Spring 2015. Search in Google Scholar

9. J. Ma, H. Zhang, S. Lou, F. Chu, Z. Shi, F. Gu, A. D. Ball, “Analytical and experimental investigation of vibration characteristics induced by tribofilm-asperity interactions in hydrodynamic journal bearings”, Mechanical Systems and Signal Processing, vol. 150, 2021.10.1016/j.ymssp.2020.107227 Search in Google Scholar

10. H. Zhang, J. Ma, X. Li, S. Xiao, F. Gu, A. Ball, “Fluid-asperity interaction induced random vibration of hydrodynamic journal bearings towards early fault diagnosis of abrasive wear”, Tribology International, vol. 160, 2021.10.1016/j.triboint.2021.107028 Search in Google Scholar

11. S. Y. Wang, D. X. Yang, H. F. Hu, “Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring”, Sensors (Basel, Switzerland), vol. 18(4), 1111, 2018.10.3390/s18041111 Search in Google Scholar

12. M. P. Appleby, “Wear debris detection and oil analysis using ultrasonic and capacitance measurements”, PhD diss., University of Akron, 2010. Search in Google Scholar

13. D. Šaravanja and M. Grbešić, “Application of Vibration Analysis in Journal Bearing Problems Diagnostics”, Annals of DAAAM & Proceedings, vol. 30, 2019.10.2507/30th.daaam.proceedings.013 Search in Google Scholar

14. S. Poddar, “Vibration & acoustic emission monitoring of cavitation, contamination & starvation in journal bearings”, PhD diss., IIT Delhi, 2020. Search in Google Scholar

15. A. C. Müller, S. Guido, Introduction to Machine Learning with Python, 2017. Search in Google Scholar

16. V. Sugumaran, V. Muralidharan, K.I. Ramachandran, “Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing”, Mechanical Systems and Signal Processing, vol. 21, pp. 930–942, 2007.10.1016/j.ymssp.2006.05.004 Search in Google Scholar

17. K.A. Pravin, R. Jegadeeshwaran, V. Sugumaran, “Roller Bearing Fault Diagnosis by Decision Tree Algorithms with Statistical Feature”, International Journal of Research in Mechanical Engineering, vol. 1, issue 1, pp. 01-09, 2013. Search in Google Scholar

18. M. Amarnath, V. Sugumaran, H. Kumar, “Exploiting sound signals for fault diagnosis of bearings using decision tree”, Measurement, vol. 46, pp. 1250-1256, 2013. Search in Google Scholar

19. V.G. Salunkhe and R.G. Desavale, “An Intelligent Prediction for Detecting Bearing Vibration Characteristics Using a Machine Learning Model”, Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, vol. 4(3), p. 031004, 2021. Search in Google Scholar

20. T.W. Rauber, A.L. da Silva Loca, F. de Assis Boldt, A.L. Rodrigues and F.M. Varejão, “An experimental methodology to evaluate machine learning methods for fault diagnosis based on vibration signals”, Expert Systems with Applications, vol. 167, p. 114022, 2021. Search in Google Scholar

21. A.M. Umbrajkaar, A. Krishnamoorthy and R.B. Dhumale, “Vibration analysis of shaft misalignment using machine learning approach under variable load conditions”, Shock and Vibration, 2020.10.1155/2020/1650270 Search in Google Scholar

22. P. C. Norton, A. Samuel, et al., Beginning Python, 2005. Search in Google Scholar

23. F. Pedregosa, G. Varoquaux, et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011. Search in Google Scholar

24. R. M. Gray, Entropy and Information Theory, Springer, 2011. Search in Google Scholar

25. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2013. Search in Google Scholar

26. Bently Nevada General Electric, Rotor Kit (10mm) Model RK4 Operation and Maintenance Manual, 2015. Search in Google Scholar

27. Bently Nevada General Electric, Rotor Kit Oil Whirl/Whip Option Model RK4 Operational Manual, 2015. Search in Google Scholar

28. PCB Piezotronics, Model 356A02 ICP Accelerometer Installation and Operating Manual, 2015. Search in Google Scholar

29. PCB Piezotronics, Model 130D21 ICP Array Microphone Installation and Operating Manual, 2015. Search in Google Scholar

30. IoTech, DaqBoard/1000 and /2000 Series User’s Manual, 2005. Search in Google Scholar

31. Measurement Computing, NI LabVIEW Support Driver Support Enhancements. Search in Google Scholar

32. Measurement Computing, DaqIO for NI LabVIEW Support VIs. Search in Google Scholar

33. A. Brandt, Noise and Vibration Analysis: Signal Analysis and Experimental Procedures, Wiley Online Library Book, 2011.10.1002/9780470978160 Search in Google Scholar

eISSN:
2083-7429
Langue:
Anglais