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

Jurnal Ilmiah SINERGI

Gigih Priyandoko¹  Dedy Usman Effendi¹  Diky Siswanto¹  Eska Riski Naufal¹  Istiadi Istiadi² 

[1] Department of Electrical Engineering, Faculty of Engineering, Universitas Widyagama;Department of Informatics, Faculty of Engineering, Universitas Widyagama;

关键词: artificial neural network;    bearing;    discrete wavelet transform;    fault diagnosis;   

DOI  :  10.22441/sinergi.2021.1.005

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown