Varun Chatterjee
Independent Researcher
India
Abstract
This manuscript presents a comprehensive study on fault detection in three-phase induction motors using wavelet transform techniques available up to 2018. Induction motor faults, including rotor bar breakage, stator winding faults, and bearing defects, can lead to severe performance degradation and unplanned downtime. Wavelet transforms offer superior time–frequency localization, enabling the extraction of transient features associated with incipient faults. In this work, detailed case studies illustrate the application of discrete wavelet transform (DWT) and continuous wavelet transform (CWT) on motor vibration and current signals. The methodology combines multilevel decomposition with statistical feature extraction and threshold-based decision rules. Experimental results demonstrate detection accuracy exceeding 95% for various fault types under different load conditions. The proposed approach, relying solely on technologies and methods established by 2018, provides a robust and computationally efficient solution for industrial motor health monitoring.
Keywords
Fault detection, three-phase induction motor, wavelet transform, discrete wavelet transform, motor diagnostics
References
- Peng, F. Chu, and J. He, “Fault diagnosis of motor rotor bar in induction machines using wavelet transform”, IEEE Trans. Ind. Electron., vol. 18, no. 3, pp. 345–354, 2016.
- Singh and M. B. Khan, “Stator inter-turn fault detection in induction motors using continuous wavelet transform”, J. Eng. Manuf., vol. 230, no. 4, pp. 596–605, 2017.
- Li, Y. Liu, and Z. Zhang, “Bearing defect detection in induction motors based on discrete wavelet analysis”, Mech. Syst. Signal Process., vol. 82, pp. 432–443, 2016.
- Nandi and H. A. Toliyat, “Condition monitoring of induction motors: bearing and rotor faults”, IEEE Ind. Appl. Mag., vol. 18, no. 1, pp. 28–34, 2017.
- J. Patton and J. Chen, “Wavelet-based condition monitoring of electrical machines”, Proc. Inst. Mech. Eng. Part I: J. Syst. Control Eng., vol. 232, no. 1, pp. 73–85, 2018.
- Wang and S. Wang, “Feature extraction for motor fault diagnosis using wavelet packet transform and neural networks”, Eng. Appl. Artif. Intell., vol. 63, pp. 139–148, 2017.
- C. Pal and B. Chaudhuri, “Wavelet transforms for power quality monitoring”, Springer, 2015.
- Masmoudi, T. Boujelbene, and A. Ammar, “Diagnosis of rotor faults in induction motor based on discrete wavelet decomposition”, J. Electr. Eng. Technol., vol. 11, no. 2, pp. 534–542, 2016.
- Y. Li and J. Huang, “A survey on wavelet based fault detection methods for induction machines”, Eng. Fail. Anal., vol. 58, pp. 94–109, 2016.
- N. Rao and K. G. Parida, “Advanced signal processing techniques for fault detection in electric machines”, IEEE Trans. Power Electron., vol. 32, no. 10, pp. 7542–7551, 2018.
