A COMPARATIVE STUDY OF DIVERSE AUTOENCODER MODELS IN LOCAL GEAR PITTING FAULT DIAGNOSIS

Abstract

Gearbox, which is one of the most important and frequently used components among mechanical power transmission systems, has often been observed to occur in gear surface pitting faults in industrial applications that require high torque. For the diagnosis of gear pitting faults, vibration analysis is one of the commonly utilized techniques. Recently, there has been an increasing interest in applying deep learning approaches for classification and learning feature representations. Deep learning provides an excellent opportunity to integrate vibration signals for gear pitting fault diagnosis. Therefore, in this study, autoencoder models Contractive Autoencoder (CAE), Sparse Autoencoder (SAE) and Variational Autoencoder (VAE) are used to extract deep feature representations of gear pitting data. Without using any additional feature extraction techniques, in this study uses the raw vibrational data directly to identify the local gear pitting faults. Experimental results have shown that Sparse Autoencoder is a viable and efficient feature extraction method and provides a new research method for gear pit fault diagnosis.

Keywords

• Contractive Autoencoder
• Sparse Autoencoder
• Variational Autoencoder
• Local Gear Pitting
• Fault Diagnosis

References

1. H. Öztürk, M. Sabuncu, and I. Yesilyurt, "Early detection of pitting damage in gears using mean frequency of scalogram," J. Vib. Control, vol. 14, 2008, pp. 469–484.
2. M. Amarnath and S. K. Lee, "Assessment of surface contact fatigue failure in a spur geared system based on the tribological and vibration parameter analysis," Measurement, vol. 76, 2015, pp. 32–44.
3. S. Bagavathiappan, B. B. Lahiri, T. Saravanan, et al., "Infrared thermography for condition monitoring – a review," Infrared Phys. Technol., vol. 60, 2013, pp. 35–55.
4. Z. Peng, N. J. Kessissoglou, and M. Cox, "A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques," Wear, vol. 258, 2005, pp. 1651–1662.
5. Y. E. Karabacak and N. G. Özmen, "Common spatial pattern-based feature extraction and worm gear fault detection through vibration and acoustic measurements," Measurement, vol. 187, 2022.
6. B. Hizarci, R. C. Ümütlü, H. Ozturk, and Z. Kıral, "Vibration region analysis for condition monitoring of gearboxes using image processing and neural networks," Exp. Tech., vol. 43, no. 6, 2019, pp. 739–755.
7. T. Toutountzakis, K. T. Chee, and M. David, "Application of acoustic emission to seeded gear fault detection," NDT E Int., vol. 38, no. 1, 2005, pp. 27–36.
8. K. Worden and J. M. Dulieu-Barton, "An overview of intelligent fault detection in systems and structures," Struct. Health Monit., vol. 3, no. 1, 2004, pp. 85–98.

9. M. H. Benbouzid, "A review of induction motors signature analysis as a medium for faults detection," IEEE Trans. Ind. Electron., vol. 47, no. 5, 2000, pp. 984–993.
10. S. Ebersbach and Z. Peng, "Expert system development for vibration analysis in machine condition monitoring," Expert Syst. Appl., vol. 34, 2008, pp. 291–299.
11. F. P. G. Marquez, A. M. Tobias, J. M. P. Perez, and M. Papaelias, "Condition monitoring of wind turbines: Techniques and methods," Renew. Energy, vol. 46, 2012, pp. 169–178.
12. J. Zarei, M. A. Tajeddini, and H. R. Karimi, "Vibration analysis for bearing fault detection and classification using an intelligent filter," Mechatronics, vol. 24, 2014, pp. 151–157.
13. A. K. S. Jardine, D. Lin, and D. Banjevic, "A review on machinery diagnostics and prognostics implementing condition-based maintenance," Mech. Syst. Signal Process., vol. 20, 2006, pp. 1483–1510.
14. R. C. Ümütlü, B. Hizarci, H. Ozturk, and Z. Kiral, "Classification of pitting fault levels in a worm gearbox using vibration visualization and ANN," Sādhanā, vol. 45, no. 1, 2020, pp. 1–13.
15. B. Samanta, "Gear fault detection using artificial neural networks and support vector machines with genetic algorithms," Mech. Syst. Signal Process., vol. 18, 2004, pp. 625–644.
16. B. Samanta, K. R. Al-Balushi, and S. A. Al-Araimi, "Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection," Eng. Appl. Artif. Intell., vol. 16, no. 7, 2003, pp. 657–665.
17. W. Huang, H. Sun, Y. Liu, and W. Wang, "Feature extraction for rolling element bearing faults using resonance sparse signal decomposition," Exp. Tech., vol. 41, 2017, pp. 251–265.
18. B. Zhang, H. Wang, Y. Tang, et al., "Residual useful life prediction for slewing bearing based on similarity under different working conditions," Exp. Tech., vol. 42, 2018, pp. 279–289.
19. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, 2006, pp. 504–507.
20. X. Chen, A. Ji, and G. Cheng, "A novel deep feature learning method based on the fused-stacked AEs for planetary gear fault diagnosis," Energies, vol. 12, no. 23, 2019.
21. Y. Qu, M. He, J. Deutsch, and D. He, "Detection of pitting in gears using a deep sparse autoencoder," Appl. Sci., vol. 7, no. 5, 2017.
22. J. Li, X. Li, D. He, and Y. Qu, "A novel method for early gear pitting fault diagnosis using stacked SAE and GBRBM," Sensors, vol. 19, no. 4, 2019.
23. X. Li, J. Li, Y. Qu, and D. He, "Gear pitting fault diagnosis using integrated CNN and GRU network with both vibration and acoustic emission signals," Appl. Sci., vol. 9, no. 4, 2019.
24. X. Li, J. Li, Y. Qu, and D. He, "Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning," Chin. J. Aeronaut., vol. 33, no. 2, 2020, pp. 418–426.
25. J. Li, X. Li, D. He, and Y. Qu, "Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor," J. Intell. Manuf., vol. 31, 2020, pp. 1899–1916.
26. S. Khan and T. Yairi, "A review on the application of deep learning in system health management," Mech. Syst. Signal Process., vol. 107, 2018, pp. 241–265.
27. S. Rifai, G. Mesnil, P. Vincent, et al., "Higher order contractive auto-encoder," in Proc. Joint Eur. Conf. Mach. Learn. Knowl. Discov. Databases, Berlin, Heidelberg, Sep. 2011, pp. 645–660, Springer.
28. C. Shen, Y. Qi, J. Wang, et al., "An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder," Eng. Appl. Artif. Intell., vol. 76, 2018, pp. 170–184.
29. L. Wen, L. Gao, and X. Li, "A new deep transfer learning based on sparse auto-encoder for fault diagnosis," IEEE Trans. Syst. Man Cybern. Syst., vol. 49, no. 1, 2017, pp. 136–144.
30. Y. Qi, C. Shen, D. Wang, J. Shi, X. Jiang, and Z. Zhu, "Stacked sparse autoencoder-based deep network for fault diagnosis of rotating machinery," IEEE Access, vol. 5, 2017, pp. 15066–15079.
31. A. He and X. Jin, "Deep variational autoencoder classifier for intelligent fault diagnosis adaptive to unseen fault categories," IEEE Trans. Rel., vol. 70, no. 4, 2021, pp. 1581–1595.
32. S. Liu, H. Jiang, Z. Wu, and X. Li, "Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis," Measurement, vol. 168, 2021, p. 108371.
33. D. Addo, S. Zhou, J. K. Jackson, G. U. Nneji, H. N. Monday, K. Sarpong, and C. A. Owusu-Agyei, "Evae-net: An ensemble variational autoencoder deep learning network for COVID-19 classification based on chest X-ray images," Diagnostics, vol. 12, no. 11, 2022, p. 2569.
34. H.Öztürk. Gearbox health monitoring and fault detection using vibration analysis. PhD Thesis, Graduate School of Natural and Applied Sciences of Dokuz Eylul University, Türkiye (2006).