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Condition Monitoring and Fault Diagnosis of Induction Motor using DWT and ANN

  • Research Article-Electrical Engineering
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Abstract

This paper presents an efficient approach to estimate the failures of various components in an induction motor using motor current signature analysis. Conventional sensor-based fault detection methods lead to huge manpower and require greater number of sensors. To overcome these drawbacks, current signature base fault detection is proposed. An advanced spectral analysis, namely discrete wavelet transform (DWT), is used for frequency domain analysis. This paper also presents fault severity estimation using feature extraction-based evaluation of DWT coefficients. As the DWT gives many coefficients at higher level decomposition which is essential for high resolution, fault classification and severity index become challenging. To address this issue, artificial neural network (ANN) algorithm is used after DWT decomposition. The fault severity is predicted by proposed fault indexing parameter of various features like energy, standard deviation, skewness, variance, RMS values. Conventional algorithms like support vector machine, k‐nearest neighbour, local mean decomposition-singular value decomposition and extreme learning machine have given maximum of 98–99% accuracy, Whereas the proposed DWT-based ANN has given 100% accuracy with tanh function. Moreover, the testing loss with this function is also very less. Experimental results have affirmed the accuracy of proposed fault detection of various faults in induction motor of rating 3—Phase, 1. 5KW, 440 V and 50 Hz.

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References

  1. Patel, R.A.; Bhalja, B.R.: Condition monitoring and fault diagnosis of induction motor using support vector machine. Electr Power Compon Syst 44(6), 683–692 (2016). https://doi.org/10.1080/15325008.2015.1131762

    Article  Google Scholar 

  2. Yang, S.K.: A condition-based failure-prediction and processing-scheme for preventive maintenance. IEEE Trans. Reliab. 52(3), 373–383 (2003). https://doi.org/10.1109/TR.2003.816402

    Article  Google Scholar 

  3. Dahi K, Elhani S, Guedira S, Sadiki L, Ouachtouk I (2015) High-resolution spectral analysis method to identify rotor faults in WRIM using Neutral Voltage. https://doi.org/10.1109/EITech.2015.7162988

  4. Wang, X.; Fang, F.: Bearing failure diagnosis in three-phase induction motor by chirp-Z transform and Zoom-MUSIC. Int Conf Electr Control Eng ICECE (2011). https://doi.org/10.1109/ICECENG.2011.6057197

    Article  Google Scholar 

  5. Cocconcelli, M.; Zimroz, R.; Rubini, R.; Bartelmus, W.: STFT based approach for ball bearing fault detection in a varying speed motor. In: Condition monitoring of machinery in non-stationary operations. Springer, Berlin, Heidelberg (2012)

    Google Scholar 

  6. Rosero, J.; Romeral, L.; Ortega, J.A.; Rosero, E.: Short circuit fault detection in PMSM by means of empirical mode decomposition (EMD) and wigner ville distribution (WVD). Conf Proc IEEE Appl Power Electron Conf Expos APEC (2008). https://doi.org/10.1109/APEC.2008.4522706

    Article  Google Scholar 

  7. Konar, P.; Chattopadhyay, P.: Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Appl Soft Comput J (2011). https://doi.org/10.1016/j.asoc.2011.03.014

    Article  Google Scholar 

  8. Kompella, K.C.D.; Mannam, V.G.R.; Rayapudi, S.R.: DWT based bearing fault detection in induction motor using noise cancellation. J Electr Syst Inf Technol (2016). https://doi.org/10.1016/j.jesit.2016.07.002

    Article  Google Scholar 

  9. Kompella, K.C.D.; Rao, M.V.G.; Rao, R.S.: SWT based bearing fault detection using frequency spectral subtraction of stator current with and without an adaptive filter. IEEE Region Annu Int Conf Proc TENCON 2017 (2017). https://doi.org/10.1109/TENCON.2017.8228277

    Article  Google Scholar 

  10. Samanta, B.; Al-Balushi, K.R.: Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process (2003). https://doi.org/10.1006/mssp.2001.1462

    Article  Google Scholar 

  11. Agrawal, P.; Jayaswal, P.: Diagnosis and classifications of bearing faults using artificial neural network and support vector machine. J Inst Eng Series C (2020). https://doi.org/10.1007/s40032-019-00519-9

    Article  Google Scholar 

  12. Kumar, R.S., et al.: A method for broken bar fault diagnosis in three phase induction motor drive system using Artificial Neural Networks. Int J Ambient Energy (2021). https://doi.org/10.1080/01430750.1934117

    Article  Google Scholar 

  13. Rai, A.; Upadhyay, S.H.: A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribol Int (2016). https://doi.org/10.1016/j.triboint.2015.12.037

    Article  Google Scholar 

  14. Singh, S.; Kumar, A.; Kumar, N.: Motor current signature analysis for bearing fault detection in mechanical systems. Procedia Materials Science (2014). https://doi.org/10.1016/j.mspro.2014.07.021

    Article  Google Scholar 

  15. Toma, R.N.; Prosvirin, A.E.; Kim, J.M.: Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors (2020). https://doi.org/10.3390/s20071884

    Article  Google Scholar 

  16. Trajin, B.; Regnier, J.; Faucher, J.: Comparison between vibration and stator current analysis for the detection of bearing faults in asynchronous drives. IET Electr Power Appl (2010). https://doi.org/10.1049/iet-epa.2009.0040

    Article  Google Scholar 

  17. Saini, M.K.; Aggarwal, A.: Detection and diagnosis of induction motor bearing faults using multiwavelet transform and naive Bayes classifier. Int Trans Electr Energy Syst (2018). https://doi.org/10.1002/etep.2577

    Article  Google Scholar 

  18. Zaman, S.M.K.; Marma, H.U.M.; Liang, X.: Broken rotor bar fault diagnosis for induction motors using power spectral density and complex continuous wavelet transform methods. IEEE Canadian Conf Electr Comput Eng CCECE 2019 (2019). https://doi.org/10.1109/CCECE.2019.8861517

    Article  Google Scholar 

  19. Hassan, O.E.; Amer, M.; Abdelsalam, A.K.; Williams, B.W.: Induction motor broken rotor bar fault detection techniques based on fault signature analysis—a review. IET Electr Power Appl (2018). https://doi.org/10.1049/iet-epa.2018.0054

    Article  Google Scholar 

  20. Chahine, K.: Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine. Int Trans Electr Energy Syst (2018). https://doi.org/10.1002/etep.2612

    Article  Google Scholar 

  21. Ramu, S.K.; Raj Irudayaraj, G.C.; Subramani, S.; Subramaniam, U.: Broken rotor bar fault detection using Hilbert transform and neural networks applied to direct torque control of induction motor drive. IET Power Electron (2020). https://doi.org/10.1049/iet-pel.2019.154

    Article  Google Scholar 

  22. Ali, M.Z.; Liang, X.: Induction motor fault diagnosis using discrete wavelet transform. 2019 IEEE Can Conf Electr Comput Eng (2019). https://doi.org/10.1109/CCECE.2019.8861923

    Article  Google Scholar 

  23. Defdaf, M.; Berrabah, F.; Chebabhi, A.; Cherif, B.D.E.: A new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis. Int Trans Electr Energy Syst (2021). https://doi.org/10.1002/2050-7038.12807

    Article  Google Scholar 

  24. Ali, M.Z.; Liang, X.: Induction motor fault diagnosis using discrete wavelet transform. 2019 IEEE Canadian Conf Electr Comput Eng (2019). https://doi.org/10.1109/CCECE.2019.8861923

    Article  Google Scholar 

  25. Tian, Y.; Ma, J.; Lu, C.; Wang, Z.: Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine. Mech Mach Theory (2015). https://doi.org/10.1016/j.mechmachtheory.2015

    Article  Google Scholar 

  26. Rauber, T.W.; de Assis Boldt, F.; Varejão, F.M.: Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE Trans Ind Electr (2014). https://doi.org/10.1109/tie.2014.2327589

    Article  Google Scholar 

  27. Du, W.; Tao, J.; Li, Y.; Liu, C.: Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mech Syst Signal Process (2014). https://doi.org/10.1016/j.ymssp.2013.09.003

    Article  Google Scholar 

  28. Sadeghian, A.; Ye, Z.; Wu, B.: Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Trans Instrum Measure (2009). https://doi.org/10.1109/tim.2009.2013743

    Article  Google Scholar 

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  1. Electrical and Electronics Engineering, National Institute of Technology Delhi, Delhi, India

    Srinivas chikkam & Sachin Singh

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  1. Srinivas chikkam
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Correspondence to Srinivas chikkam.

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chikkam, S., Singh, S. Condition Monitoring and Fault Diagnosis of Induction Motor using DWT and ANN. Arab J Sci Eng 48, 6237–6252 (2023). https://doi.org/10.1007/s13369-022-07294-3

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