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Modeling of Fault Frequencies for Distributed Damages in Bearing Raceways

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Abstract

There are two types of damages in the inner ring and outer ring of the bearing. One is names as localized damage and second is known as distributed damage. These damages in inner ring or outer ring create bearing faults and causes breakdown on machine. To avoid the unplanned shutdown, various type of preventive maintenance techniques such as thermal analysis, acoustic emission and vibration analysis are used in the industry. However, these conventional methods use costly sensors and require experts for data interpretation and analysis. Recently, an alternative approach has been proposed by researchers which is known as Park analysis technique. The data collection and analysis through Park analysis technique is cheaper and easier as compared to thermal analysis, acoustic emission and vibration analysis techniques. However, Park analysis technique utilizes the frequency domain analysis and need a frequency information of the fault to locate fault amplitude on the spectrum. The frequency models are only available for the bearing localized damages and the frequency model for the bearing distributed damages are still unknown. Thus, derivation of frequency model for bearing distributed damage is required to enhance the fault diagnosing capability of the Park analysis technique. Hence, this research paper aims to derive a mathematical model of the fault frequencies related to bearing distributed damages. The developed model will be experimentally verified through experiments performed on the custom designed test-setup.

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References

  1. Dalvand, F., Kang, M.: Detection of generalized-roughness and single point bearing fault using linear prediction-based current noise cancellation. IEEE Trans. Ind. Electron. 65(12), 9728–9738 (2018)

    Article  Google Scholar 

  2. Leite, V.C.M.N., da Silva, J.G.B., Francimeir, G., da Silva, L.E.B., Lambert-Torres, G., Bonaldi, E.L., de Oliveira, L.E.L.: Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current. IEEE Trans. Ind. Electron. 62(3), 1855–1865 (2015)

    Article  Google Scholar 

  3. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Magzoub, M.: An intelligent fault diagnosis of induction motors in an arbitrary noisy environment. J. Nondestr. Eval. 35, 12 (2016)

    Article  Google Scholar 

  4. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Hung, N.T.: Analysis of bearing outer race defects in induction motors. In: The 5th International Conference on Intelligent and Advanced Systems (ICIAS) (2014)

  5. Li, Y., Xu, M., Liang, X., Huang, W.: Application of bandwidth EMD and bdaptive multi-scale morphology analysis for incipient fault diagnosis of rolling bearings. IEEE Trans. Ind. Electron. 64(8), 6506–6517 (2017)

    Article  Google Scholar 

  6. Soualhi, A., Clerc, G., Razik, H.: Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique. IEEE Trans. Ind. Electron. 60(9), 4053–4062 (2013)

    Article  Google Scholar 

  7. Riera-Guasp, M., Antonino-Daviu, J.A., Capolino, G.A.: Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: State of the art. IEEE Trans. Ind. Electron. 62(3), 1746–1759 (2015)

    Article  Google Scholar 

  8. Dalvand, F., Keshavarzi, M., Kalantar, A., Cheraghdar, A.: Detection of generalized-roughness bearing fault using statistical-time indices of instantaneous frequency of motor voltage space vector. In: The 23rd Iranian Conference on Electrical Engineering (ICEE) (2015)

  9. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S.: Diagnosis of distributed faults in outer race of bearings via Park’s Transformation method. In: The 10th Asian Control Conference (ASCC) Kota Kinabalu, Malaysia (2015)

  10. Shah, D.S., Patel, V.N.: A review of dynamic modeling and fault identifications methods for rolling element bearing. In: The 2nd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME (2014)

  11. Dolenc, B., Boškoski, P., Juričić, Đ.: Distributed bearing fault diagnosis based on vibration analysis. Mech. Syst. Signal Process. 66–67, 521–532 (2015)

    Google Scholar 

  12. Gao, Z., Cecati, C., Ding, S.X.: A survey of fault diagnosis and fault-tolerant techniques part I: Fault diagnosis with model based and signal-based approaches. IEEE Trans. Industr. Electron. 62(6), 3757–3767 (2015)

    Article  Google Scholar 

  13. Hurtado, Z.Y.M., Tello, C.P., Sarduy, J.G.: A review on detection and fault diagnosis in induction machines. Publicaciones en Ciencias y Tecnologa 8(01), 11–30 (2014)

    Google Scholar 

  14. Dolenc, B., Boškoski, P., Pfajfar, J., Juričić, Đ.: Vibration based diagnosis of distributed bearing faults. In: Vibration Engineering and Technology of Machinery, Proceedings of VETOMAC X, University of Manchester, UK (2014)

  15. Al-Ghamda, A.M., Mba, D.: A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech. Syst. Signal Process. 20(7), 1537–1571 (2006)

    Article  Google Scholar 

  16. Sawalhi, N., Randall, R.B.: Vibration response of spalled rolling element bearings: observations, simulations and signal processing techniques to track the spall size. Mech. Syst. Signal Process. 25(3), 846–870 (2011)

    Article  Google Scholar 

  17. Kumar, R., Singh, M.: Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal. Measurement 46(1), 537–545 (2013)

    Article  Google Scholar 

  18. Eftekharnejad, B., Charnley, B., Carrasco, M.R.: The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing. Mech. Syst. Signal Process. 25(1), 266–284 (2011)

    Article  Google Scholar 

  19. de Castelbajac, C., Ritou, M., Laporte, S., Furet, B.: Monitoring of distributed defects on hsm spindle bearings. Appl. Acoust. 77, 159–168 (2014)

    Article  Google Scholar 

  20. Kulkarni, S., Bewoor, A.: Vibration based condition assessment of ball bearing with distributed defects. J. Meas. Eng. 4(2), 87–94 (2016)

    Google Scholar 

  21. Sharma, S., Abed, W., Sutton, R., Subudhi, B.: Corrosion fault diagnosis of rolling element bearing under constant and variable load and speed conditions. Int. Fed. Autom. Control 48(30), 049–054 (2016)

    Google Scholar 

  22. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Alwadie, A., Aman, M.: Analysis of distributed faults in inner and outer race of bearing via park vector analysis method. Neural Comput. Appl. 31(1), 683–691 (2017)

    Google Scholar 

  23. Irfan, M.: A novel non-intrusive method to diagnose bearings surface roughness faults in induction motors. J. Fail. Anal. Prev. 18(1), 145–152 (2018)

    Article  Google Scholar 

  24. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Alwadie, A., Aman, M.: An Assessment on the Non-invasive Methods for Condition Monitoring of Induction Motors. Fault Diagnosis and Detection. InTech Publishing (2017). ISBN 978-953-51-5011-4

  25. Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V.S., Magzoub, M.: A non-invasive method for condition monitoring of induction motors operating under arbitrary loading conditions. Arab. J. Sci. Eng. 41(9), 3463–3471 (2016)

    Article  Google Scholar 

  26. Kuruppu, S.S., Kulatunga, N.A.: D-Q current signature-based faulted phase localization for SM-PMAC machine drives. IEEE Trans. Industr. Electron. 62(1), 113–121 (2015)

    Article  Google Scholar 

  27. Aman, M., Saad, N., Nor, N.B.M., Tahir, S., Irfan, M.: An unsupervised automated method to diagnose induction motor faults. J. Fundam. Appl. Sci. 10(7) (2018)

  28. Saad, N., Irfan, M., Ibrahim, R.: Condition Monitoring and Faults Diagnosis of Induction Motors: Electrical Signature Analysis. CRC Press & Routledge - Taylor & Francis Group (2018). ISBN 9780815389958

  29. Alwadie, A.: The decision making system for condition monitoring of induction motors based on vector control model. Machines 5(4), 27 (2017)

    Article  Google Scholar 

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Acknowledgements

The author acknowledge the support from the Deanship of Scientific Research, Najran University Saudi Arabia for the award of research fund NU/ESCI/16/036.

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Correspondence to Muhammad Irfan.

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Appendix

Appendix

$$ \begin{aligned} {\text{Fit factor}} & = 1 + \frac{{{\text{D}}_{{{\text{ir}}}} }}{{1.8\left( {{\text{D}}_{{{\text{or}}}} - {\text{D}}_{{{\text{ir}}}} } \right) + 6{\text{D}}_{{{\text{ir}}}} }} \\ & = 1 + \frac{{15}}{{1.8\left( {35 - 15} \right) + 90}} = 1.12 \\ \end{aligned} $$

\( {\text{F}}_{\text{ddi}} \) for the no load:

$$ {\text{F}}_{\text{ddi}} \left( {\text{Higher limit}} \right) = {\text{F}}_{\text{ddi}} * {\text{Fit factor}} = { 89}. 2* 1. 1 2 { } = { 1}00{\text{ Hz}} $$
$$ {\text{F}}_{\text{ddi}} \left( {\text{Lower limit}} \right) = {\text{F}}_{\text{ddi}} /{\text{Fit factor}} = { 89}. 2/ 1. 1 2 { } = { 79}. 6 {\text{ Hz}} $$

Thus, the \( {\text{F}}_{\text{ddi}} \) under no load condition should be in the range of 79 Hz to 100 Hz


\( {\text{F}}_{\text{ddi}} \) for the full load:

$$ {\text{F}}_{\text{ddi}} \left( {\text{Higher limit}} \right) = {\text{F}}_{\text{ddi}} * {\text{Fit factor}} = { 83}. 3 6* 1. 1 2 { } = { 93}. 3 6 {\text{ Hz}} $$
$$ {\text{F}}_{\text{ddi}} \left( {\text{Lower limit}} \right) = {\text{F}}_{\text{ddi}} /{\text{Fit factor}} = { 83}. 3 6/ 1. 1 2 { } = { 74}. 4 {\text{ Hz}} $$

Thus, the \( {\text{F}}_{\text{ddi}} \) under full load condition should be in the range of 74–93 Hz


\( {\text{F}}_{\text{ddo}} \) for the no load:

$$ {\text{F}}_{\text{ddo}} \left( {\text{Higher limit}} \right) = {\text{F}}_{\text{ddo}} * {\text{Fit factor}} = { 2}0. 50* 1. 1 2 { } = {\text{ 23 Hz}} $$
$$ {\text{F}}_{\text{ddo}} \left( {\text{Lower limit}} \right) = {\text{F}}_{\text{ddo}} /{\text{Fit factor}} = { 2}0. 50/ 1. 1 2 { } = {\text{ 18 Hz}} $$

Thus, the \( {\text{F}}_{\text{ddo}} \) under no load condition should be in the range of 18–23 Hz


\( {\text{F}}_{{{\text{dd}}0}} \) for the full load:

$$ {\text{F}}_{{{\text{dd}}0}} \left( {\text{Higher limit}} \right) = {\text{F}}_{\text{ddo}} * {\text{Fit factor}} = { 19}. 1 4* 1. 1 2 { } = { 21}. 4 {\text{ Hz}} $$
$$ {\text{F}}_{\text{ddo}} \left( {\text{Lower limit}} \right) = {\text{F}}_{\text{ddo}} /{\text{Fit factor}} = { 19}. 1 4/ 1. 1 2 { } = {\text{ 17 Hz}} $$

Thus, the \( {\text{F}}_{\text{ddo}} \) under full load condition should be in the range of 17 Hz to 21 Hz

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Irfan, M. Modeling of Fault Frequencies for Distributed Damages in Bearing Raceways. J Nondestruct Eval 38, 98 (2019). https://doi.org/10.1007/s10921-019-0635-0

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