Monitoring Gangguan Beban Motor Induksi Menggunakan Neural Network

Witjaksono, Herdjandjam (2002) Monitoring Gangguan Beban Motor Induksi Menggunakan Neural Network. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Motor induksi banyak digunakan dalam industri karena kokoh serta andal dibandingkan dengan motor DC. Monitoring gangguan beban motor induksi dengan menggunakan sensor torsi akan sulit dideteksi, untuk menutup kelemahan tersebut dan menghasilkan monitoring gangguan beban yang cepat, dibuat sistem monitoring dengan metode unsupervised neural network. Dari simulasi yang telah dibuat didapatkan hasil bahwa metode unsupervised neural network selain mampu menampilkan pola gangguan beban, juga mampu mengklasifikasikan pola gangguan beban. Dengan demikian, monitoring gangguan beban pada motor induksi tidak memerlukan sensor torsi lagi, tetapi cukup dilihat dari monitor komputer.
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Induction motor is widely used in industries because it is more rugged and reliable than DC motors. It is hard to monitor induction motor load disturbances using sensors especially in harsh environments such as toxic or radioactive ones. In order to solve the problems and provide a fast load disturbance monitoring, a monitoring system with unsupervised neural network method has been used. From the result of the simulation that has been made, monitoring load disturbance with unsupervised neural network method has been able to show the pattern of load disturbance and classify the kinds of load disturbances pattern. Thus, monitoring load disturbance on induction motor does not necessary need a torque sensor, but seeing enough personal computer monitor is sufficient.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Motor Induksi, Monitoring Gangguan Beban, dan Unsupervised Neural Network , Induction motor, load disturbance monitoring, and unsupervised neural network.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2681.B47 Electric motors, Direct current.
Divisions:	Faculty of Industrial Technology > Electrical Engineering > 20101-(S2) Master Thesis
Depositing User:	magang .
Date Deposited:	25 Sep 2026 07:55
Last Modified:	25 Sep 2026 07:55
URI:	http://repository.its.ac.id/id/eprint/144908

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