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Incipient Fault Detection of Full Ceramic Ball Bearing Based on Modified Observer

  • Regular Papers
  • Control Theory and Applications
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Abstract

As a key part of the energy transmission chain, full ceramic ball bearing elements are considered as important components in ultraprecise rotating machines, which is required as a special attention in order to avoid expensive production shutdown due to the appearance of massive failures. Therefore, it is necessary to detect the appearance of incipient faults for full ceramic ball bearing elements by implementing an appropriate model. In this paper, an observer-based incipient fault detection method for full ceramic ball bearings is proposed. Firstly, the mechanism model of full ceramic ball bearing with incipient faults is established, and it is converted into a discrete-time model based on the zero-order hold equivalent method. Secondly, a modified observer is proposed as a residual generator, which has more design degrees of the freedom than the Luenberger observer. Thirdly, the l1/H∞ performance is introduced to enhance the disturbance robustness and incipient fault sensitivity. In addition, the design of an adaptive threshold can effectively avoid fault false alarms. Finally, the effectiveness of the proposed method is verified by numerical simulation.

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References

  1. H. Walter, D. Marco, and M. Roger, “Failure analysis of a ceramic ball race bearing made of Y-TZP zirconia,” Engineering Failure Analysis, vol. 36, pp. 262–268, 2014.

    Article  Google Scholar 

  2. H.-T. Shi, X.-T. Bai, K. Zhang, Z.-N. Wang, and Z.-M. Liu, “Spalling localization on the outer ring of hybrid ceramic ball bearings based on the sound signals,” IEEE Access, vol. 7, pp. 134621–134634, 2019.

    Article  Google Scholar 

  3. L.-S. Leao, A.-A. Cavalini, T.-S. Morais, G.-P. Melo, and V. Steffen, “Fault detection in rotating machinery by using the modal state observer approach,” Journal of Sound and Vibration, vol. 458, pp. 123–142, 2019.

    Article  Google Scholar 

  4. H.-T. Shi, Y.-J. Shang, X.-C. Zhang, and Y.-H. Tang, “Research on the initial fault prediction method of rolling bearings based on DCAE-TCN transfer learning,” Shock and Vibration, vol. 2021, Article ID 5587756, 2021.

  5. J. Zhang, A.-K. Swain, and S.-K. Nguang, “Detection and isolation of incipient sensor faults for a class of uncertain non-linear systems,” IET Control Theroy and Applications, vol. 6, pp. 1870–1880, 2012.

    Article  MathSciNet  Google Scholar 

  6. J.-L. Gomez, I. Khelf, and A. Bourdon, “Angular modeling of a rotating machine in non-stationary conditions: Application to monitoring bearing defects of wind turbines with instantaneous angular speed,” Mechanism and Machine Theory, vol. 136, pp. 27–51, 2019.

    Article  Google Scholar 

  7. W.-J. Sun, R. Zhao, and R.-Q. Yan, “Convolutional discriminative feature learning for induction motor fault diagnosis,” IEEE Transactions on Industrial Informatics, vol. 13, pp. 1350–1359, 2017.

    Article  Google Scholar 

  8. H.-T. Shi and X.-T. Bai, “Model-based uneven loading condition monitoring of full ceramic ball bearings in starved lubrication,” Mechanical Systems and Signal Processing, vol. 139, 106583, 2019.

    Article  Google Scholar 

  9. H.-T. Shi, X.-T. Bai, K. Zhang, Y.-H. Wu, and G.-D. Yue, “Influence of uneven loading condition on the sound radiation of starved lubricted full ceramic ball bearings,” Journal of Sound and Vibration, vol. 461, 114910, 2019.

    Article  Google Scholar 

  10. J. Chen and R.-J. Patton, Robust Model-based Fault Diagnosis for Dynamic Systems, Kluwer, London, pp. 1–22, 1999.

    Book  Google Scholar 

  11. D.-G. Luenberger, “Observing the state of a linear system,” IEEE Transactions on Military Electronics, vol. 8, no. 1, pp. 74–80, 1964.

    Article  Google Scholar 

  12. W.-X. Han, Z.-H. Wang, and Y. Shen, “H−/L∞ fault detection observer design for uncertain linear systems,” International Journal of Systems Science, vol. 50, no. 2, pp. 283–293, 2019.

    Article  MathSciNet  Google Scholar 

  13. S.-M. Tabatabaeipour and T. Bak, “Robust observer-based fault estimation and accommodation of discrete-time piecewise linear systems,” Journal of the Franklin Institute, vol. 351, pp. 277–295, 2014.

    Article  MathSciNet  Google Scholar 

  14. I.-U. Khan, D. Wagg, and N.-D. Sims, “Nonlinear robust observer design using an invariant manifold approach,” Control Engineering Practice, vol. 55, pp. 69–79, 2016.

    Article  Google Scholar 

  15. Q.-H. Zhang, “Adaptive Kalman filter for actuator fault diagnosis,” Automatica, vol. 93, pp. 333–342, 2018.

    Article  MathSciNet  Google Scholar 

  16. S. Chebotarev, D. Efimov, and T. Raissi, “Interval observers for continuous-time LPV systems with L1/L2 performance,” Automatica, vol. 58, pp. 82–89, 2015.

    Article  Google Scholar 

  17. A. Chibani, M. Chadli, P. Shi, and N.-B. Braiek, “Fuzzy fault detection filter design for T-S fuzzy systems in the finite-frequency domain,” IEEE Transactions on Fuzzy Systems, vol. 25, pp. 1051–1061, 2017.

    Article  Google Scholar 

  18. Y. Gu and G.-H. Yang, “Fault detection for discrete-time Lipschitz non-linear systems in finite-frequency domain,” IET Control Theory and Applicatons, vol. 11, pp. 2177–2186, 2017.

    Article  MathSciNet  Google Scholar 

  19. X.-J. Li and G.-H. Yang, “Fault detection in finite frequency domain for Takagi—Sugeno fuzzy systems with sensor faults,” IEEE Transactions on Cybernetics, vol. 44, pp. 1446–1458, 2014.

    Article  Google Scholar 

  20. J.-L. Wang, G.-H. Yang, and J. Liu, “An LMI approach to H− index and mixed H−/H∞ fault detection observer design,” Automatica, vol. 43, pp. 1656–1665, 2007.

    Article  Google Scholar 

  21. M. Zhong, S.-X. Ding, J. Lam, and H. Wang, “An LMI approach to design robust fault detection filter for uncertain LTI systems,” Automatica, vol. 39, pp. 543–550, 2003.

    Article  MathSciNet  Google Scholar 

  22. S.-A. Raka and C. Combastel, “Fault detection based on robust adaptive thresholds: A dynamic interval approach,” Annual Reviews in Control, vol. 37, pp. 119–128, 2013.

    Article  Google Scholar 

  23. A. Johansson, M. Bask, and T. Norlander, “Dynamic threshold generators for robust fault detection in linear systems with parameter uncertainty,” Automatica, vol. 42, pp. 1095–1106, 2006.

    Article  MathSciNet  Google Scholar 

  24. A.-Q. Khan and S.-X. Ding, “Threshold computation for fault detection in a class of discrete-time nonlinear systems,” International Journal of Adaptive Control & Signal Processing, vol. 25, no. 5, pp. 407–429, 2011.

    Article  MathSciNet  Google Scholar 

  25. J.-T. Li, Z.-H. Wang, W.-H. Zhang, T. Raissib, and Y. Shen, “Interval observer design for continuous-time linear parameter-varying systems,” Systems & Control Letters, vol. 134, p. 104541, 2019.

    Article  MathSciNet  Google Scholar 

  26. Z.-H. Zhang and G.-H. Yang, “Distributed fault detection and isolation for multiagent systems: An interval observer approach,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 6, pp. 2220–2230, 2020.

    Article  Google Scholar 

  27. F.-S. Yang, H.-G. Zhang, B. Jiang, and X.-D. Liu, “Adaptive reconfigurable control of systems with time-varying delay against unknown actuator faults,” International Journal of Adaptive Control and Signal Processing, vol. 28, no. 11, pp. 1206–1226, 2014.

    Article  MathSciNet  Google Scholar 

  28. M.-X. Hou and H.-T. Shi, “Stator-winding incipient shorted-turn fault detection for motor system in motorized spindle using modified interval observers,” IEEE Transactions on Instrumentation and Measurement, vol. 70, Article no. 3505716, 2021.

  29. R.-R. Li and G.-H. Yang, “Distributed optimization for a class of uncertain MIMO nonlinear multi-agent systems with arbitrary relative degree,” Information Sciences, vol. 501, pp. 55–77, 2020.

    MathSciNet  Google Scholar 

  30. M. Chadli, A. Abdo, and S.-X. Ding, “H−/H∞ fault detection filter design for discrete-time Takagi—Sugeno fuzzy system,” Automatica, vol. 49, pp. 1996–2005, 2013.

    Article  MathSciNet  Google Scholar 

  31. R.-R. Li and G.-H. Yang, “Consensus control of a class of uncertain nonlinear multiagent systems via gradient-based algorithms,” IEEE Transactions on Cybernetics, vol. 49, pp. 2085–2094, 2019.

    Article  Google Scholar 

  32. D.-W. Wang, H. Wang, and X. Li, “H−/H∞ fault detection observer design for two-dimensional Roesser systems,” Systems & Control Letters, vol. 82, pp. 115–120, 2015.

    Article  MathSciNet  Google Scholar 

  33. E.-T. Idriss and E. Jantunen, “Fault analysis of the wear fault development in rolling bearings,” Engineering Failure Analysis, vol. 57, pp. 470–482, 2015.

    Article  Google Scholar 

  34. K.-K. Zhang, B. Jiang, and X.-G. Yan, “Incipient sensor fault estimation and accommodation for inverter devices in electric railway traction systems,” International Journal of Adaptive Control and Signal, vol. 31, pp. 785–804, 2017.

    Article  MathSciNet  Google Scholar 

  35. H.-T. Shi, Z.-M. Liu, X.-T. Bai, Y.-P. Li, and Y.-H. Wu, “A theoretical model with the effect of cracks in the local spalling of full ceramic ball bearings,” Applied Sciences, vol. 9, pp. 4142–4162, 2019.

    Article  Google Scholar 

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Funding

This research was funded by National Science Foundation of China grant number 52075348, 51905357, 52005352, National Key R&D Plan grant number No:2017YFC0703903.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang, 10153, China

    Huaitao Shi & Yuhou Wu

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Maxiao Hou

  3. Department of Mathematics, University of Washington, Washington, 5246, USA

    Baicheng Li

Authors
  1. Huaitao Shi
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  2. Maxiao Hou
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  3. Yuhou Wu
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  4. Baicheng Li
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Corresponding author

Correspondence to Huaitao Shi.

Additional information

Huaitao Shi was born in Anhui Province, China, in 1982. He received his B.S. degree from Northeastern University, Shenyang, China, in 2005, and a Ph.D. degree in control theory and control engineering at Northeastern University, Shenyang, China, in 2012. Since 2012, he has been a professor with Mechanical Engineering and Automation, Shenyang Jianzhu University, Shenyang, Liaoning Province, China. His current research interests include incipient fault detection.

Maxiao Hou was born in Henan Province, China, in 1996. He received his B.S. degree in software enginerring from Dalian Jiaotong University, Dalian, China, in 2018. where he is currently pursuing a Ph.D. degree in mechanical engineering from Xi’an Jiaotong University. His current research interests include incipient fault detection and intelligent spindle.

Yuhou Wu was born in Liaoning Province, China, in 1955. He received his B.S. degree in mechanical engineering from Shenyang Jianzhu University, Shenyang, Liaoning Province, China, in 1982, and a Ph.D. degree in mechanical engineering at Northeastern University, Shenyang, China, in 1994. Since 1994, he has been a Professor, Ph.D. supervisor and the head of the Institute of Mechanical Engineering and Automation in Shenyang Jianzhu University. He has authored or co-authored more than 80 SCI/EI papers. He has been a leading talent of Liaoning Province and academician candidate of China National Engineering research Institute and president of Shenyang Jianzhu University. His current research interests include incipient fault detection and fault prediction.

Baicheng Li was born in Liaoning Province, China. He is currently an undergraduate in the Department of Mathematics at the University of Washington. His current research interests include algebra and incipient fault detection.

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Shi, H., Hou, M., Wu, Y. et al. Incipient Fault Detection of Full Ceramic Ball Bearing Based on Modified Observer. Int. J. Control Autom. Syst. 20, 727–740 (2022). https://doi.org/10.1007/s12555-021-0167-0

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  • DOI: https://doi.org/10.1007/s12555-021-0167-0

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