DIAGNOSIS OF INDUCTION MOTOR BEARING DEFECT USING DISCRETE WAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORK

Gigih Priyandoko; Istiadi Istiadi; Diky Siswanto; Dedy Usman Effendi; Eska Riski Naufal

doi:10.22441/sinergi.2021.1.005

Authors

Gigih Priyandoko Department of Electrical Engineering, Faculty of Engineering, Universitas Widyagama, Indonesia
Istiadi Istiadi Department of Informatics, Faculty of Engineering, Universitas Widyagama, Indonesia
Diky Siswanto Department of Electrical Engineering, Faculty of Engineering, Universitas Widyagama, Indonesia
Dedy Usman Effendi Department of Electrical Engineering, Faculty of Engineering, Universitas Widyagama, Indonesia
Eska Riski Naufal Department of Electrical Engineering, Faculty of Engineering, Universitas Widyagama, Indonesia

DOI:

https://doi.org/10.22441/sinergi.2021.1.005

Keywords:

Artificial Neural Network, Bearing, Discrete Wavelet Transform, Fault diagnosis,

Abstract

Induction motor is electromechanical equipment that is widely used in various industrial applications. The research paper presents the detection of the defect to three-phase induction motor bearing using discrete wavelet transforms and artificial neural networks to detect whether or not the motor is damaged. An experimental test rig was made to obtain data on healthy phase currents or damaged bearings on the induction motor using the motor current signature analysis (MCSA) method. Several mother-level wavelets are chosen on the wavelet method from the obtained current signal. The feature of the wavelet results is used as an input of the Artificial Neural Network to classify the condition of the induction motor. The results showed that the system could provide an accurate diagnosis of the condition of the induction motor.