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Servo axis incipient degradation assessment of CNC machine tools using the built-in encoder

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Abstract

Servo axis system has been widely applied in the high-precision CNC machine tools. Its performance degradation may lead to the degradation of whole machine and ultimately result to the accuracy degradation of parts manufacturing. Thus, health assessment of the servo axis is very essential, especially for those in-service CNC machine tools. However, restricted by the complexity of servo axis structure, weak signal of incipient degradation, and limited sensors’ installation space, traditional degradation evaluation methods, such as the vibration based scheme, are very difficult to be applied in real service environment directly. In this paper, a new methodology is established for servo axis incipient degradation assessment by reusing the position fluctuation information captured by built-in encoder. Firstly, to highlight the torsional behavior of the servo axis components, the instantaneous angular acceleration (IAA) is estimated by using the position fluctuation information with frequency domain weighting (FDW) method. After that, the wavelet packet transform (WPT) is employed for decomposition of these nonstationary IAA signals. Finally, a Gini index (GI)–guided denoising scheme is established for incipient degradation feature reconstruction. The effectiveness of the proposed method is investigated by simulations; thereafter, it is applied for the X-axis assessment of an in-service high-precision vertical machining center. All the results illustrate that the proposed method is sensitive to incipient degradation of the rotating components and offers an alternative solution for health assessment of servo axis.

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References

  1. Zhang K, Yuen A, Altintas Y (2013) Pre-compensation of contour errors in five-axis CNC machine tools. Int J Mach Tools Manuf 74:1–11

    Article  Google Scholar 

  2. Law M, Altintas Y, Srikantha Phani A (2013) Rapid evaluation and optimization of machine tools with position-dependent stability. Int J Mach Tools Manuf 68:81–90

    Article  Google Scholar 

  3. Mori M, Fujishima M (2013) Remote monitoring and maintenance system for CNC machine tools. Procedia CIRP 12:7–12

    Article  Google Scholar 

  4. Jin X, Que Z, Sun Y, Guo Y, Qiao W (2019) A data-driven approach for bearing fault prognostics. IEEE Trans Ind Appl 55(4):3394–3401

    Article  Google Scholar 

  5. Cao H, He D, Xi S, Chen X (2018) Vibration signal correction of unbalanced rotor due to angular speed fluctuation. Mech Syst Signal Process 107:202–220

    Article  Google Scholar 

  6. Li Y, Zhou S (2019) A new instantaneous wavelet bicoherence for local fault detection of rotating machinery

  7. Cai G, Selesnick IW, Wang S, Dai W, Zhu Z (2018) Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis. J Sound Vib 432:213–234

    Article  Google Scholar 

  8. Verl A, Frey S (2010) Correlation between feed velocity and preloading in ball screw drives. CIRP Ann Manuf Technol 59(1):429–432

    Article  Google Scholar 

  9. Verl A, Heisel U, Walther M, Maier D (2009) Sensorless automated condition monitoring for the control of the predictive maintenance of machine tools. CIRP Ann 58(1):375–378

    Article  Google Scholar 

  10. Bourogaoui M, Sethom HBA, Belkhodja IS (2017) Real-time encoder faults detection and rotor position estimation for permanent magnet synchronous motor drives fault tolerant sensorless control using digital signal controller. Math Comput Simul 131:253–267

    Article  MathSciNet  Google Scholar 

  11. Li P, Jia X, Feng J, Davari H, Qiao G, Hwang Y, Lee J (2018) Prognosability study of ball screw degradation using systematic methodology. Mech Syst Signal Process 109:45–57

    Article  Google Scholar 

  12. Zhou Y, Mei X, Jiang G, Sun N, Tao T (2010) Sensorless evaluation for a computer numerical control machine tool slide level using an empirical mode decomposition method. Proc Inst Mech Eng C J Mech Eng Sci 224(3):721–730

    Article  Google Scholar 

  13. Li Y, Zhou S (2018) Sensorless balance method for the spindle system of computer numerical control gear grinding machine. Meas Control 51(9–10):460–469

    Article  Google Scholar 

  14. Gubran AA, Sinha JK (2014) Shaft instantaneous angular speed for blade vibration in rotating machine. Mech Syst Signal Process 44(1):47–59

    Article  Google Scholar 

  15. Shao Y, Su D, Al-Habaibeh A, Yu W (2016) A new fault diagnosis algorithm for helical gears rotating at low speed using an optical encoder. Measurement 93:449–459

    Article  Google Scholar 

  16. Zhao M, Lin J (2018) Health assessment of rotating machinery using a rotary encoder. IEEE Trans Ind Electron 65(3):2548–2556

    Article  Google Scholar 

  17. Gomez JL, Khelf I, Bourdon A, André H, Rémond D (2019) Angular modeling of a rotating machine in non-stationary conditions: application to monitoring bearing defects of wind turbines with instantaneous angular speed. Mech Mach Theory 136:27–51

    Article  Google Scholar 

  18. Lai J-Y, Lin K-Y, Tseng S-J, Ueng W-D (2008) On the development of a parametric interpolator with confined chord error, feedrate, acceleration and jerk. Int J Adv Manuf Technol 37(1):104–121

    Article  Google Scholar 

  19. Bertoti E, Yamashita RY, Eckert JJ, Santiciolli FM, Dedini FG, Silva LCA. Application of pattern recognition for the mitigation of systematic errors in an optical incremental encoder, Proceedings of the 10th International Conference on Rotor Dynamics – IFToMM, pp 65–78

    Google Scholar 

  20. Incze II, Szabó C, Imecs M (2010) In: Rudas IJ, Fodor J, Kacprzyk J (eds) Incremental encoder in electrical drives: modeling and simulation, Computational intelligence in engineering. Springer Berlin Heidelberg, Berlin, pp 287–300

    MATH  Google Scholar 

  21. Zhao M, Jia X, Lin J, Lei Y, Lee J (2018) Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox. Mech Syst Signal Process 98:16–31

    Article  Google Scholar 

  22. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen N-C, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Math Phys Eng Sci 454(1971):903–995

    Article  MathSciNet  Google Scholar 

  23. Lazhari M, Sadhu A (2019) Decentralized modal identification of structures using an adaptive empirical mode decomposition method. J Sound Vib 447:20–41

    Article  Google Scholar 

  24. Xing Z, Qu J, Chai Y, Tang Q, Zhou Y (2017) Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine. J Mech Sci Technol 31(2):545–553

    Article  Google Scholar 

  25. Sun H, Zhang X, Wang J (2016) Online machining chatter forecast based on improved local mean decomposition. Int J Adv Manuf Technol 84(5):1045–1056

    Google Scholar 

  26. Li L, Huawei W, Yong Z Research on the identification of crack status through the axle acoustic emission signal based on local mean decomposition and grey correlation analysis. Adv Acous Emission Technol:101–117

  27. Qiao N, Wang L-h, Liu Q-y, Zhai H-q (2019) Multi-scale eigenvalues empirical mode decomposition for geomagnetic signal filtering. Measurement 146:885–891

    Article  Google Scholar 

  28. Pan H, Zheng J, Liu Q (2019) A novel roller bearing condition monitoring method based on RHLCD and FVPMCD. IEEE Access 7:96753–96763

    Article  Google Scholar 

  29. Tang G, Wang X, He Y (2016) Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution. J Mech Sci Technol 30(1):43–54

    Article  Google Scholar 

  30. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  31. Chen G, Li Q-Y, Li D-Q, Wu Z-Y, Liu Y (2019) Main frequency band of blast vibration signal based on wavelet packet transform. Appl Math Model 74:569–585

    Article  Google Scholar 

  32. Gini C (1921) Measurement of inequality of incomes. Econ J 31(121):124–126

    Article  Google Scholar 

  33. Banerjee AK (2019) Economic properties of statistical indices: the case of a multidimensional Gini index: a correction and an addition. J Quant Econ

  34. Hurley N, Rickard S. Comparing measures of sparsity. pp 55–60

  35. Lin J, Qu L (2000) Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J Sound Vib 234(1):135–148

    Article  Google Scholar 

  36. Li Y, Lin J, Wang X, Lei Y (2014) Biphase randomization wavelet bicoherence for mechanical fault diagnosis. Measurement 49:407–420

    Article  Google Scholar 

  37. Sui X, Wan K, Zhang Y (2019) Pattern recognition of SEMG based on wavelet packet transform and improved SVM. Optik 176:228–235

    Article  Google Scholar 

  38. García Plaza E, Núñez López PJ (2018) Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations. Mech Syst Signal Process 98:902–919

    Article  Google Scholar 

Download references

Funding

The work is supported by the National Natural Science Foundation of China (No. 51505147 and No. 51875434) and partly supported by the Fundamental Research Funds for the Central Universities (No. 222201514315), which is highly appreciated by the authors.

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Authors and Affiliations

  1. School of Mechanical and Power Engineering, Key Laboratory of Pressure Systems and Safety (Ministry of Education), East China University of Science and Technology, Shanghai, China

    Yong Li & Shaoping Zhou

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

    Ming Zhao

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  1. Yong Li
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  2. Ming Zhao
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  3. Shaoping Zhou
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Correspondence to Yong Li.

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Li, Y., Zhao, M. & Zhou, S. Servo axis incipient degradation assessment of CNC machine tools using the built-in encoder. Int J Adv Manuf Technol 106, 4293–4305 (2020). https://doi.org/10.1007/s00170-019-04901-w

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  • DOI: https://doi.org/10.1007/s00170-019-04901-w

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