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Feature Extraction of Hob Vibration Signals Using Denoising Method Combining VMD and Grey Relational Analysis

  • Research Article-Electrical Engineering
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Abstract

Vibration analysis is an effective approach to evaluate hob wear status and diagnose hob faults. However, the extraction of vibration signal features is susceptible to noise interference. To solve this problem, a method combining grey relational analysis (GRA) and variational mode decomposition (VMD), named GVMD, is proposed in this paper. In our method, the mode number K, the most important parameter of VMD, can be adaptively determined by GRA. After VMD decomposition, GRA is again used to distinguish noise-dominant modes and signal-dominant modes, in which noise-dominant modes are processed by soft thresholding method. Then, the processed noise-dominant modes and signal-dominant modes are reconstructed to obtain the denoised signal, and signal features can be accurately extracted. In experiments, simulation signals, hob wear vibration signals and hob broken tooth vibration signals are used to evaluate performance of GVMD and other methods. The results demonstrate that GVMD achieves better results than other methods. GVMD can eliminate noise interference and effectively extract signal features.

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References

  1. Liu, Z.W.; He, Z.J.; Guo, W.; Tang, Z.C.: A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery. ISA Trans. 61, 211–220 (2016)

    Article  Google Scholar 

  2. Zheng, K.; Luo, J.F.; Zhang, Y.; Li, T.L.; Wen, J.F.; Xiao, H.: Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD. ISA Trans. 89, 256–271 (2019)

    Article  Google Scholar 

  3. Hu, C.F.; Wang, Y.X.: Multidimensional denoising of rotating machine based on tensor factorization. Mech. Syst. Signal Process. 122, 273–289 (2019)

    Article  Google Scholar 

  4. Sadooghi, M.S.; Khadem, S.E.: A new performance evaluation scheme for jet engine vibration signal denoising. Mech. Syst. Signal Process. 76–77, 201–212 (2016)

    Article  Google Scholar 

  5. Chen, Y.L.; Zhang, P.L.; Wang, Z.J.; Yang, W.C.; Yang, Y.D.: Denoising algorithm for mechanical vibration signal using quantum Hadamard transformation. Measurement 66, 168–175 (2015)

    Article  Google Scholar 

  6. Yue, G.D.; Cui, X.S.; Zou, Y.Y.; Bai, X.T.; Wu, Y.H.; Shi, H.T.: A Bayesian wavelet packet denoising criterion for mechanical signal with non-Gaussian characteristic. Measurement 138, 702–712 (2019)

    Article  Google Scholar 

  7. Dudik, J.M.; Coyle, J.L.; EI-Jaroudi, A.; Sun, M.G.; Sejdic, E.: A matched dual-tree wavelet denoising for tri-axial swallowing vibrations. Biomed. Signal Process. Control 27, 112–121 (2016)

    Article  Google Scholar 

  8. Bi, F.R.; Ma, T.; Wang, X.: Development of a novel knock characteristic detection method for gasoline engines based on wavelet-denoising and EMD decomposition. Mech. Syst. Signal Process. 117, 517–536 (2019)

    Article  Google Scholar 

  9. Wang, H.C.; Chen, J.; Dong, G.G.: Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform. Mech. Syst. Signal Process. 48, 103–119 (2014)

    Article  Google Scholar 

  10. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.C.; Tung, C.C.; Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. A 454, 903–995 (1998)

    MathSciNet  MATH  Google Scholar 

  11. Yang, G.; Liu, Y.; Wang, Y.; Zhu, Z.: EMD interval thresholding denoising based on similarity measure to select relevant modes. Signal Process. 109, 95–109 (2015)

    Article  Google Scholar 

  12. Ma, B.; Zhang, T.: Single-channel blind source separation for vibration signals based on TVF-EMD and improved SCA. IET Signal Proc. 14(4), 259–268 (2020)

    Article  Google Scholar 

  13. Lu, W.Q.; Zhang, L.B.; Liang, W.; Yu, X.C.: Research on a small-noise reduction method based on EMD and its application in pipeline leakage detection. J. Loss Prev. Process Ind. 41, 282–293 (2016)

    Article  Google Scholar 

  14. Wu, Z.H.; Huang, N.E.: Ensemble empirical mode decomposition: a noise assisted data analysis method. Adv. Adapt. Data Anal. 1, 1–41 (2019)

    Article  Google Scholar 

  15. Wang, W.Y.; Chen, Q.J.; Yan, D.L.; Geng, D.Z.: A novel comprehensive evaluation method of the draft tube pressure pulsation of Francis turbine based on EEMD and information entropy. Mech. Syst. Signal Process. 116, 772–786 (2019)

    Article  Google Scholar 

  16. Peng, Y.X.; Liu, Y.S.; Zhang, C.; Wu, L.: Ensemble EMD-based signal denoising using modified interval thresholding. Arab. J. Sci. Eng. (2021)

  17. Mariyappa, N.; Sengottuvel, S.; Parasakthi, C.; Gireesan, K.; Janawadkar, M.P.; Radhakrishnan, T.S.; Sundar, C.S.: Baseline drift removal and denoising of MCG data using EEMD: Role of noise amplitude and the thresholding effect. Med. Eng. Phys. 36, 1266–1276 (2014)

    Article  Google Scholar 

  18. Liu, Y.Y.; Yang, G.L.; Li, M.; Yin, H.L.: Variational mode decomposition denoising combined the detrended fluctuation analysis. Signal Process. 125, 349–364 (2016)

    Article  Google Scholar 

  19. Dragomiretskiy, K.; Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process 62, 531–544 (2014)

    Article  MathSciNet  Google Scholar 

  20. Zhang, Y.G.; Chen, B.; Pan, G.F.; Zhao, Y.: A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Convers. Manage. 195, 180–197 (2019)

    Article  Google Scholar 

  21. Li, J.M.; Yao, X.F.; Wang, H.; Zhang, J.F.: Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis. Mech. Syst. Signal Process. 126, 568–589 (2019)

    Article  Google Scholar 

  22. Xiao, Q.Y.; Li, J.; Zeng, Z.M.: A denoising scheme for DSPI phase based on improved variational mode decomposition. Mech. Syst. Signal Process. 110, 28–41 (2018)

    Article  Google Scholar 

  23. Abdoos, A.A.: A new intelligent method based on combination of VMD and ELM for short term wind power forecasting. Neurocomputing 203, 111–120 (2016)

    Article  Google Scholar 

  24. Hu, H.P.; Zhang, L.M.; Yan, H.C.; Bai, Y.P.; Wang, P.: Denoising and baseline drift removal method of MEMS hydrophone signal based on VMD and wavelet threshold processing. IEEE Access 7, 59913–59922 (2019)

    Article  Google Scholar 

  25. Xiao, F.Y.; Yang, D.C.; Guo, X.H.; Wang, Y.: VMD-based denoising methods for surface electromyography signals. J. Neural Eng. 16(5), 056017 (2019)

    Article  Google Scholar 

  26. Zhang, J.; He, J.; Long, J.; Yao, M.; Zhou, W.: A new denoising method for UHF PD signals using adaptive VMD and SSA-based shrinkage method. Sensors 19, 7 (2019)

    Google Scholar 

  27. Ren, H.; Liu, W.Y.; Shan, M.C.; Wang, X.: A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning. Measurement 148, (2019)

  28. Si, D.; Gao, B.; Guo, W.; Yan, Y.; Tian, G.Y.; Yin, Y.: Variational mode decomposition linked wavelet method for EMAT denoise with large lift-off effect. NDT & E Int. 107, (2019)

  29. Lu, J.Y.; Yue, J.K.; Zhu, L.J.; Li, G.F.: Variational mode decomposition denoising combined with improved Bhattacharyya distance. Measurement 151, 02632241 (2020)

    Article  Google Scholar 

  30. Cui, J.; Yu, R.Z.; Zhao, D.B.; Yang, J.Y.; Ge, W.C.; Zhou, X.M.: Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods. Appl. Energy 247, 480–491 (2019)

    Article  Google Scholar 

  31. Li, J.; Chen, Y.; Qian, Z.H.; Lu, C.G.: Research on VMD based adaptive denoising method applied to water supply pipeline leakage location. Measurement 151, 02632241 (2020)

    Google Scholar 

  32. Deng, J.L.: Control problems of grey systems. Syst. Control Lett. 1, 288–294 (1982)

    Article  MathSciNet  Google Scholar 

  33. Deng, J.L.: Introduction to grey system theory. J. Grey Syst. 1, 1–24 (1989)

    MathSciNet  MATH  Google Scholar 

  34. Li, H.J.; Suen, C.Y.: A novel Non-local means image denoising method based on grey theory. Pattern Recogn. 49, 237–248 (2016)

    Article  Google Scholar 

  35. Wojciechowski, S.; Maruda, R.W.; Krolczyk, G.M.; Niesłony, P.: Application of signal to noise ratio and grey relational analysis to minimize forces and vibrations during precise ball end milling. Precis. Eng. 51, 582–596 (2018)

    Article  Google Scholar 

  36. Kanchana, J.; Prasath, V.; Krishnaraj, V.; Geetha, P.B.: Multi response optimization of process parameters using grey relational analysis for milling of hardened Custom 465 steel. Procedia Manufact. 30, 451–458 (2019)

    Article  Google Scholar 

  37. Yadav, S.K.; Yadav, S.K.: Optimization of machining parameters during the ECCDG of inconel 718 using PCA based grey relational analysis. Materials Today: Proceedings (2020)

  38. Wang, H.; Zhang, Y.M.; Yang, Z.: A risk evaluation method to prioritize failure modes based on failure data and a combination of fuzzy sets theory and grey theory. Eng. Appl. Artif. Intell. 82, 216–225 (2019)

    Article  Google Scholar 

  39. Canbolat, A.S.; Bademlioglu, A.H.; Arslanoglu, N.; Kaynakli, O.: Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods. J. Clean. Prod. 229, 874–885 (2019)

    Article  Google Scholar 

  40. Li, X.Y.; Wang, Z.P.; Zhang, L.; Zou, C.F.; Dorrell, D.D.: State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis. J. Power Sour. 410, 106–114 (2019)

    Article  Google Scholar 

  41. Wu, D.L.; Zhou, P.; Zhou, C.Q.: Evaluation of pulverized coal utilization in a blast furnace by numerical simulation and grey relational analysis. Appl. Energy 250, 1686–1695 (2019)

    Article  Google Scholar 

  42. Arıcı, E.; Kelestemur, O.: Optimization of mortars containing steel scale using Taguchi based grey relational analysis method. Constr. Build. Mater. 214, 232–241 (2019)

    Article  Google Scholar 

  43. Wu, Y.L.; Zhou, F.; Kong, J.Z.: Innovative design approach for product design based on TRIZ, AD, fuzzy and Grey relational analysis. Computers & Industrial Engineering 140, (2020)

  44. Terrien, J.; Marque, C.; Karlsson, B.: Automatic detection of mode mixing in empirical mode decomposition using non-stationarity detection: Application to selecting IMFs of interest and denoising. J. Adv. Signal Process. 2011(1), 37–44 (2011)

    Article  Google Scholar 

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Acknowledgements

The research work reported in this paper was supported by the National Key Research and Development Project (No. 2019YFB1703700).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Yachao Jia, Guolong Li & Xin Dong

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  1. Yachao Jia
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  2. Guolong Li
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  3. Xin Dong
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Correspondence to Guolong Li.

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Jia, Y., Li, G. & Dong, X. Feature Extraction of Hob Vibration Signals Using Denoising Method Combining VMD and Grey Relational Analysis. Arab J Sci Eng 47, 2925–2942 (2022). https://doi.org/10.1007/s13369-021-05951-7

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  • DOI: https://doi.org/10.1007/s13369-021-05951-7

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