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All Journal EMITTER International Journal of Engineering Technology Jurnal Elemen
Budi Suhendro
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Energy Engineering

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RANCANG BANGUN SISTEM KENDALI PENGGERAK FLYING MODE PADA PROBE ULTRASONIC TEST Budi Suhendro; Ahmad Rahmadya A; Muhammad Khoiri
ELEMEN : JURNAL TEKNIK MESIN Vol 6 No 2 (2019)
Publisher : POLITALA PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (385.812 KB) | DOI: 10.34128/je.v6i2.109

Abstract

Pengujian material dengan NDT di bidang industri saat ini masih menggunakan probe ultrasonic test secara manual. Pada penelitian ini dilakukan pembuatan sistem kontrol penggerak pada probe ultrasonic test dengan menggunakan prinsip computer numeric control (CNC) 3 axis yaitu x, y, dan z. Pergerakan 3 sumbu tersebut menggunakan motor stepper dan perangkat keras Arduino yang dikontrol oleh LabVIEW guna memberi input sekaligus memberikan hasil pergerakkan pada motor stepper. Hasil pengujian mode kecepatan 3 menunjukkan nilai RMSE mendekati nilai 0 yang artinya bahwa semakin nilai RMSE kecil maka data tersebut memiliki kemiripan dengan nilai setpoint yang diinginkan.
Early Detection of Ball Bearing Faults Using the Decision Tree Method Istanto, Iwan; Sulaiman , Robi; Rio Natanael Wijaya; Budi Suhendro; Rokhmat Arifianto; Slamet
EMITTER International Journal of Engineering Technology Vol 12 No 2 (2024)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v12i2.920

Abstract

Bearings are one of the important components in the machine that functions as a holder and positions the shaft alignment radially when rotating. Statistics show that about 50% of failures in electric motors are related to bearings. Therefore, monitoring bearing performance and efficiency before damage occurs is necessary to avoid more serious damage and save repair costs. This research aims to build a classification model that can identify bearings in normal condition and 6 types of damage (inner crack, outer crack, ball crack, and a combination of both) using the HUST dataset. The model building process begins with collecting datasets, processing and extracting dataset features, building classification models and evaluating the models that have been made. A decision tree is a type of supervised machine learning used to categorize or make predictions based on how a previous set of questions were answered. The model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization. The results of the decision tree model that has been built are able to identify bearing damage with an accuracy of 94.47%.
Co-Authors Ahmad Rahmadya A Istanto, Iwan Muhammad Khoiri Rio Natanael Wijaya Rokhmat Arifianto Slamet Sulaiman , Robi