Cloud based bearing fault diagnosis of induction motors

Abstract

Abstract -- In general, induction motors predictive maintenance is well suited for small to large-scale industries to minimize failure, maximize performance, and improve reliability. The vibration of an induction motor was investigated in this paper in order to gather precise details that can be used to forecast motor bearing failure. With this in view, an induction motor carrying fault detection scheme has been attempted. machine learning algorithms in addition to wavelet transform (WT) and fast fourier transform (FFT), an advanced signal processing technique, are used in this study to analyze frame vibrations during initialization. the Internet of Things (IoT) is at the core of today's accelerated technological growth. A large number of items are interconnected efficiently, particularly in industrial-automation, resulting in condition and monitoring to boost efficiency to capture and process the parameters of induction motor, the proposed approach uses an IoT-based platform. The details gathered can be saved in the cloud platform and viewed via a web page.

Keywords

• Induction motor
• fault diagnosis
• bearing faults
• wavelet transform
• Fast fourier transform.

References

1. [1] Z. Peroutka, T. Glasberger, and M. Janda, “Main problems and proposed solutions to induction machine drive control of multisystem locomotive,” in 2009 IEEE Energy Conversion Congress and Exposition, 2009, pp. 430–437. doi: 10.1109/ECCE.2009.5316403.
2. [2] C. Chen and C. Mo, “A method for intelligent fault diagnosis of rotating machinery,” Digital Signal Processing, vol. 14, no. 3, pp. 203–217, 2004.
3. [3] S. Poyhonen, P. Jover, and H. Hyotyniemi, “Signal processing of vibrations for condition monitoring of an induction motor,” in First International Symposium on Control, Communications and Signal Processing, 2004., 2004, pp. 499–502.
4. [4] W. R. Finley, M. M. Hodowanec, and W. G. Holter, “An analytical approach to solving motor vibration problems,” in Industry Applications Society 46th Annual Petroleum and Chemical Technical Conference (Cat. No. 99CH37000), 1999, pp. 217–232.
5. [5] S. Abbasion, A. Rafsanjani, A. Farshidianfar, and N. Irani, “Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine,” Mechanical systems and signal processing, vol. 21, no. 7, pp. 2933–2945, 2007.
6. [6] K. K. Shukla and A. K. Tiwari, Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems. Springer Science & Business Media, 2013.
7. [7] L. Song, H. Wang, and P. Chen, “Vibration-based intelligent fault diagnosis for roller bearings in low-speed rotating machinery,” IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 8, pp. 1887–1899, 2018.
8. [8] I. Attoui, N. Boutasseta, N. Fergani, B. Oudjani, and A. Deliou, “Vibration-based bearing fault diagnosis by an integrated DWT-FFT approach and an adaptive neuro-fuzzy inference system,” in 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), 2015, pp. 1–6.

9. [9] C.-Y. Lee and T.-A. Le, “Optimised approach of feature selection based on genetic and binary state transition algorithm in the classification of bearing fault in BLDC motor,” IET Electric Power Applications, vol. 14, no. 13, pp. 2598–2608, 2020.
10. [10] A. K. Verma, S. Radhika, and S. v Padmanabhan, “Wavelet based fault detection and diagnosis using online MCSA of stator winding faults due to insulation failure in industrial induction machine,” in 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 2018, pp. 204–208.
11. [11] Z. Wang, L. Yao, and Y. Cai, “Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine,” Measurement, vol. 156, p. 107574, 2020.
12. [12] J. Shawe-Taylor and N. Cristianini, Support vector machines, vol. 2. Cambridge university press Cambridge, 2000.
13. [13] J. S. L. Senanayaka, S. T. Kandukuri, H. van Khang, and K. G. Robbersmyr, “Early detection and classification of bearing faults using support vector machine algorithm,” in 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD), 2017, pp. 250–255.
14. [14] F. Li, G. Meng, L. Ye, and P. Chen, “Wavelet transform-based higher-order statistics for fault diagnosis in rolling element bearings,” Journal of Vibration and Control, vol. 14, no. 11, pp. 1691–1709, 2008.
15. [15] G. Niu, Data-driven technology for engineering systems health management. Springer, 2017.