Perancangan Sistem Deteksi Dan Diagnosis Misalignment Berbasis Fuzzy Logic Pada Motor Induksi

Arfani, Muhammad Naufal (2021) Perancangan Sistem Deteksi Dan Diagnosis Misalignment Berbasis Fuzzy Logic Pada Motor Induksi. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pada penelitian tugas akhir ini dilakukan perancangan sistem deteksi dan diagnosis berbasis fuzzy logic untuk mendeteksi dan menentukan besar derajat misalignment yang terjadi pada motor induksi. Perancangan sistem diagnosis memiliki tiga tahapan, dimulai dengan teknik Fast Fourier Transform untuk merubah output sinyal motor induksi dengan variabel yakni arus Q1 (iQ1) dari domain waktu menjadi domain frekuensi. Kemudian Findpeaks Function digunakan untuk mengetahui lokasi frekuensi dan besar amplitudo peak variabel arus Q1 pada peak f+2fr yang disimbolkan dalam LiQ1f dan PiQ1f. Terakhir Fuzzy Logic digunakan untuk menentukan besar derajat sudut angular misalignment dengan input amplitudo peak (PiQ1f) dan berdasarkan kepada 6 membership function serta 6 rule base. Hasil sistem diagnosis adalah settling time sistem diagnosis pada sudut 1° dan 2° adalah sebesar 10 detik, sedangkan pada sudut 3°, 4° dan 5° memiliki settling time sistem diagnosis sebesar 9 detik. Untuk nilai error sistem diagnosis terbesar ketika kondisi settling time adalah sudut 3° dengan error sebesar 3,17% sedangkan untuk error sistem diagnosis terkecil didapatkan oleh semua sudut karena pada waktu 10 detik semua sudut tidak terdapat error.
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In this final project, a detection and diagnosis system based on fuzzy logic is designed to detect and determine the degree of misalignment that occurs in an induction motor. The design of the diagnostic system has three stages, starting with the Fast Fourier Transform technique to change the induction motor signal output with a variable, namely the current Q1 (iQ1) from the time domain to the frequency domain. Then the Findpeaks function is used to determine the location of the frequency and amplitude of the current variable peak Q1 at peak f + 2fr which is symbolized in LiQ1f and PiQ1f. Finally, Fuzzy Logic is used to determine the degree of angular misalignment with input peak amplitude (PiQ1f) and based on 6 membership functions and 6 rule bases. The result of the diagnosis system is that the diagnosis system settling time at an angle of 1° and 2° is 10 seconds, while at an angle of 3°, 4° and 5°, the diagnosis system settling time is 9 seconds. The largest error value for the diagnosis system is when the settling time condition is 3° with an error of 3.17%, while the smallest error for the diagnosis system is obtained by all angles because at 10 seconds all angles have no errors.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Angular Misalignment, Fast Fourier Transform, Findpeaks Function, Fuzzy Logic, Motor Induksi, Angular Misalignment, Fast Fourier Transform, Findpeaks Function, Fuzzy Logic, Induction Motor.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2785 Electric motors, Induction.
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Muhammad Naufal Arfani
Date Deposited:	06 Mar 2021 05:57
Last Modified:	06 Mar 2021 05:57
URI:	http://repository.its.ac.id/id/eprint/83659

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