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An intelligent self-adaptive bearing fault diagnosis approach based on improved local mean decomposition

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Abstract

Bearings are one of the crucial elements in rotating machinery and their malfunctioning is the major reason of machine failure. The identification and diagnosing of bearing performance deterioration is critical for the smooth and reliable operation of rotating equipment’s. This paper proposes an intelligent vibration-based condition monitoring and fault diagnosis methodology for detecting the bearing defects. Experimental vibration data acquired for different bearing and operating conditions are analysed to establish a framework for identification and diagnosis of bearing faults to assess the machine health. Fault diagnosis is carried out using Improved Local Mean Decomposition (ILMD) for decomposition of the vibration signal. The vibration features extracted from the obtained pre-processed signal were selected using Principal Component Analysis (PCA) to remove the redundant features. Subsequently, these relevant features were provided as input to the machine learning methods, namely Random Forest (RF), Party Kit (PK) and Support Vector Machines (SVM) for the detection and classification of the different bearing defects. Experimental outcomes demonstrate that the proposed methodology has an enormous potential to avoid the unplanned breakdowns, which are caused by bearing failure in rotary machinery.

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References

  1. Yu, G.: A concentrated time-frequency analysis tool for bearing fault diagnosis. IEEE Trans. Instrum. Meas. 69(2), 371–381 (2020). https://doi.org/10.1109/TIM.2019.2901514

    Article  Google Scholar 

  2. Goyal, D., Choudhary, A., Pabla, B.S., Dhami, S.S.: Support vector machines based non-contact fault diagnosis system for bearings. J. Intell. Manuf. 31(5), 1275–1289 (2020). https://doi.org/10.1007/s10845-019-01511-x

    Article  Google Scholar 

  3. Choudhary, A., Goyal, D., Shimi, S.L., Akula, A.: Condition monitoring and fault diagnosis of induction motors: a review. Arch. Comput. Methods Eng. 26(4), 1221–1238 (2019). https://doi.org/10.1007/s11831-018-9286-z

    Article  Google Scholar 

  4. Mehta, A., Goyal, D., Choudhary, A., Pabla, B.S., Belghith, S.: Machine learning-based fault diagnosis of self-aligning bearings for rotating machinery using infrared thermography. Math. Probl. Eng. (2021). https://doi.org/10.1155/2021/9947300

    Article  Google Scholar 

  5. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. London. Ser. A Math. Phys. Eng. Sci. 454(1971), 903–995 (1998). https://doi.org/10.1098/rspa.1998.0193

    Article  MathSciNet  MATH  Google Scholar 

  6. Sharma, A., Verma, P., Choudhary, A., Mathew, L., Chatterji, S.: Application of wavelet analysis in condition monitoring of induction motors. Advances in electromechanical technologies, pp. 795–807. Springer, Singapore (2021)

    Google Scholar 

  7. Yang, Y., Yu, D., Cheng, J.: A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Meas. J. Int. Meas. Confed. 40(9–10), 943–950 (2007). https://doi.org/10.1016/j.measurement.2006.10.010

    Article  Google Scholar 

  8. Rilling, G., Flandrin, P.: One or two frequencies? the empirical mode decomposition answers. IEEE Trans. Signal Process. 56(1), 85–95 (2008). https://doi.org/10.1109/TSP.2007.906771

    Article  MathSciNet  MATH  Google Scholar 

  9. Wu, Z., Huang, N.E.: A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. London. Ser. A Math. Phys. Eng. Sci. 460(2046), 1597–1611 (2004). https://doi.org/10.1098/rspa.2003.1221

    Article  MATH  Google Scholar 

  10. Kopsinis, Y., McLaughlin, S.: Enhanced empirical mode decomposition using a novel sifting-based interpolation points detection. In: IEEE workshop on statistical signal processing proceedings, pp. 725–729. (2007). https://doi.org/10.1109/SSP.2007.4301354

  11. Long, S.S., Zhang, T.B., Long, F.: Causes and solutions of overshoot and undershoot and end swing in Hilbert-Huang transform. Acta Seismol. Sin. English Ed. 18(5), 602–610 (2005). https://doi.org/10.1007/s11589-005-0039-3

    Article  Google Scholar 

  12. Cheng, J., Yu, D., Yang, Y.: Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform. Mech. Syst. Signal Process. 21(3), 1197–1211 (2007). https://doi.org/10.1016/j.ymssp.2005.09.005

    Article  Google Scholar 

  13. Dätig, M., Schlurmann, T.: Performance and limitations of the Hilbert-Huang transformation (HHT) with an application to irregular water waves. Ocean Eng. 31(14–15), 1783–1834 (2004). https://doi.org/10.1016/j.oceaneng.2004.03.007

    Article  Google Scholar 

  14. Li, Y., Xu, M., Liang, X., Huang, W.: Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings. IEEE Trans. Ind. Electron. 64(8), 6506–6517 (2017). https://doi.org/10.1109/TIE.2017.2650873

    Article  Google Scholar 

  15. Smith, J.S.: The local mean decomposition and its application to EEG perception data. J. R. Soc. Interface 2(5), 443–454 (2005). https://doi.org/10.1098/rsif.2005.0058

    Article  Google Scholar 

  16. Gao, Y., Villecco, F., Li, M., Song, W.: Multi-scale permutation entropy based on improved LMD and HMM for rolling bearing diagnosis. Entropy 19(4), 176 (2017). https://doi.org/10.3390/e19040176

    Article  Google Scholar 

  17. Li, Y., Liang, X., Yang, Y., Xu, M., Huang, W.: Early fault diagnosis of rotating machinery by combining differential rational spline-based LMD and K-L divergence. IEEE Trans. Instrum. Meas. 66(11), 3077–3090 (2017). https://doi.org/10.1109/TIM.2017.2664599

    Article  Google Scholar 

  18. Wang, Z., Han, Z., Gu, F., Gu, J.X., Ning, S.: A novel procedure for diagnosing multiple faults in rotating machinery. ISA Trans. 55, 208–218 (2015). https://doi.org/10.1016/j.isatra.2014.09.006

    Article  Google Scholar 

  19. Wang, Y., He, Z., Zi, Y.: A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis. Meas. Sci. Technol. 20(2), 025704 (2009). https://doi.org/10.1088/0957-0233/20/2/025704

    Article  Google Scholar 

  20. Liu, H., Han, M.: A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings. Mech. Mach. Theory 75, 67–78 (2014). https://doi.org/10.1016/j.mechmachtheory.2014.01.011

    Article  Google Scholar 

  21. Song, R., Chen, X.: Analysis of fiber optic gyroscope vibration error based on improved local mean decomposition and kernel principal component analysis. Appl. Opt. 56(8), 2265 (2017). https://doi.org/10.1364/ao.56.002265

    Article  Google Scholar 

  22. Roy, S.S., Dey, S., Chatterjee, S.: Autocorrelation aided random forest classifier-based bearing fault detection framework. IEEE Sens. J. 20(18), 10792–10800 (2020). https://doi.org/10.1109/JSEN.2020.2995109

    Article  Google Scholar 

  23. Goyal, D., Chaudhary, A., Dang, R.K., Pabla, B.S., Dhami, S.S.: Condition monitoring of rotating machines: a review. World Sci. News 113, 98–108 (2018)

    Google Scholar 

  24. Goyal, D., Dhami, S.S. and Pabla, B.S.: Vibration response-based intelligent non-contact fault diagnosis of bearings. ASME J. Nondestruct. Eval. 4(2), 021006 (2021). https://doi.org/10.1115/1.4049371

  25. Konar, P., Chattopadhyay, P.: Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl. Soft Comput. J. 11(6), 4203–4211 (2011). https://doi.org/10.1016/j.asoc.2011.03.014

    Article  Google Scholar 

  26. Hu, Q., Si, X.S., Zhang, Q.H., Qin, A.S.: A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests. Mech. Syst. Signal Process. 139, 106609 (2020). https://doi.org/10.1016/j.ymssp.2019.106609

    Article  Google Scholar 

  27. Choudhary, A., Shimi, S.L., Akula, A.: Bearing fault diagnosis of induction motor using thermal imaging. In: International conference on computing, power and communication technologies (GUCON), pp. 950–955. IEEE (2018)

  28. Hothorn, T., Zeileis, A.: Partykit: a toolkit for recursive partytioning. J. Mach. Learn. Res. 16(118), 3905–3909 (2015)

  29. Hothorn, T., Zeileis, A.: partykit: a modular toolkit for recursive partytioning in R. J. Mach. Learn. Res. 16(1), 3905–3909 (2015)

    MathSciNet  MATH  Google Scholar 

  30. Choudhary, A., Goyal, D., Letha, S.S.: Infrared thermography-based fault diagnosis of induction motor bearings using machine learning. IEEE Sens. J. 21(2), 1727–1734 (2020)

    Article  Google Scholar 

  31. Sandhu, J.K., Verma, A.K., Rana, P.S.: An expert approach for data flow prediction: case study of wireless sensor networks. Wirel. Pers. Commun. 112(1), 325–352 (2020). https://doi.org/10.1007/s11277-020-07028-4

    Article  Google Scholar 

  32. Wang, Y., He, Z., Zi, Y.: A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis. J. Vib. Acoust. Trans. ASME 132(2), 0210101–02101010 (2010). https://doi.org/10.1115/1.4000770

    Article  Google Scholar 

  33. Cheng, J., Yang, Y., Yang, Y.: A rotating machinery fault diagnosis method based on local mean decomposition. Digit. Signal Process. A Rev. J. 22(2), 356–366 (2012). https://doi.org/10.1016/j.dsp.2011.09.008

    Article  MathSciNet  Google Scholar 

  34. Malhi, A., Gao, R.X.: PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Meas. 53(6), 1517–1525 (2004). https://doi.org/10.1109/TIM.2004.834070

    Article  Google Scholar 

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Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Deepam Goyal & Prateek Srivastava

  2. School of Interdisciplinary Research (SIRe), Indian Institute of Technology Delhi, New Delhi, India

    Anurag Choudhary

  3. Department of Computer Sciences and Engineering, Chandigarh University, Gharuan, Punjab, India

    Jasminder Kaur Sandhu

  4. Department of Mechanical Engineering, GLA University, Mathura, UP, India

    Kuldeep Kumar Saxena

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  1. Deepam Goyal
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  2. Anurag Choudhary
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  3. Jasminder Kaur Sandhu
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  4. Prateek Srivastava
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  5. Kuldeep Kumar Saxena
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Correspondence to Deepam Goyal.

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Goyal, D., Choudhary, A., Sandhu, J.K. et al. An intelligent self-adaptive bearing fault diagnosis approach based on improved local mean decomposition. Int J Interact Des Manuf (2022). https://doi.org/10.1007/s12008-022-01001-0

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