Identifikasi Kerusakan Bearing Motor Induksi Berdasarkan Karakteristik Arus Stator Dengan Metode Artificial Neural Network

., Rahmat (2016) Identifikasi Kerusakan Bearing Motor Induksi Berdasarkan Karakteristik Arus Stator Dengan Metode Artificial Neural Network. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Dalam studi ini dibahas sistem deteksi kerusakan bearing motor induksi 3 fasa rotor sangkar. Kerusakan bearing direkonstruksi dalam 3 jenis kerusakan yang tiap jenis mempunyai kondisi keruasakan yang bervariasi. Karakteristik arus stator akibat kerusakan bearing pada outer race, inner race, dan ball dianalisis menggunakan teknik Fast Fourier Transform (FFT). Karakteristik arus akibat kerusakan bearing tersebut dianalisa menggunakan metode Artificial Neural Network untuk mengklasifikasi jenis kerusakan bearing pada motor induksi 3 fasa. Dengan metode ini dapat dihasilkan suatu analisa identifikasi dan klasifikasi kerusakan bearing yang lebih akurat. ======================================================================================================== In this study discussed bearing damage detection system 3-phase induction motor rotor cage. Bearing damage reconstructed in three types of damage that each type has varied damage condition. Stator current characteristics due to damage to the bearing outer race, inner race, and ball are analyzed using Fast Fourier Transform (FFT). Current characteristics due to bearing damage was analyzed using Artificial Neural Network for classifying types of bearing damage on 3-phase induction motor. This method may produce an analysis of the identification and classification of bearing damage is more accurate.

Item Type: Thesis (Masters)
Additional Information: RTE 621.822 Rah i 3100016068323
Uncontrolled Keywords: Arus stator, Bearing, Identifikasi, Fast Fourier transform, Artificial neural network
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2785 Electric motors, Induction.
Divisions: Faculty of Industrial Technology > Electrical Engineering > 20101-(S2) Master Thesis
Depositing User: Yeni Anita Gonti
Date Deposited: 07 Jul 2020 06:35
Last Modified: 07 Jul 2020 06:35
URI: http://repository.its.ac.id/id/eprint/76324

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