论文信息 - Fault Classification of Induction Motor Using Discrete Wavelet Transform and Fuzzy Inference System

Fault Classification of Induction Motor Using Discrete Wavelet Transform and Fuzzy Inference System

This paper develops a real-time condition-monitoring algorithm for induction motor. The condition monitoring mechanism is based on the discrete wavelet transform (DWT) and the fuzzy inference system (FIS). In this method, the stator currents are used as an input to the system. No direct access to the induction motor is required. The developed system has been rigorously assessed theoretically and experimentally, and it has been shown that the system is robust and reliable.

Kuspijani Kuspijani | Richa Watiasih | Prihastono Prihastono

[1] Bahi Tahar,et al. On-Line Monitoring and Classification of Stator windings Faults inInduction Machine Using Fuzzy Logic and ANFIS Approach , 2016 .

[2] A. Dimarogonas. Vibration for engineers , 1992 .

[3] G. Dhole,et al. Signal Processing based Wavelet Approach for Fault Detection of Induction Motor , 2014 .

[4] Samsul Bahari Mohd Noor,et al. Broken Rotor Bar Fault Detection and Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network , 2017 .

[7] Aleena Swetapadma,et al. Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines , 2015, Comput. Intell. Neurosci..