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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik EMITTER International Journal of Engineering Technology
Rio Natanael Wijaya
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Computer Science & IT Engineering

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Perhitungan dan Penentuan Jenis Aliran pada Untai FASSIP-03 NT Saat Komisioning Berdasarkan Variasi Daya Pemanas Dedy Haryanto; Ainur Rosidi; G. Bambang Heru K; Giarno Giarno; Mulya Juarsa; Totok Dermawan; Rio Natanael Wijaya; Yadi Yunus
Prosiding Seminar Sains Nasional dan Teknologi Vol 12, No 1 (2022): VOL 12, NO 1 (2022): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v12i1.7196

Abstract

Kejadian station blackout (SBO) pada PLTN Fukushima Daiichi pada Maret 2011 di Jepang menjadi latar belakang yang penting untuk kegiatan penelitian tentang sistem pendinginan pasif. Pengaruh perubahan densitas fluida di daerah panas menimbulkan gaya apung (buoyancy force) dan pengaruh perubahan densitas fluida pada keadaan dingin menimbulkan gaya gravitasi (gravitational force) sehingga terjadi sirkulasi alam pada fluida kerja (air) di sepanjang untai. Tujuan penelitian dilakukan untuk mengetahui batasan operasi sehingga terjadi sirkulasi alami dan menentukan jenis aliran yang terjadi berdasarkan hasil perhitungan. Penelitian dilakukan secara eksperimental berdasarkan variasi setting temperatur air dalam tangki pemanas dan daya listrik di heater (variasi tegangan regulator). Analisis dilakukan berdasarkan grafik laju aliran sirkulasi alam yang terjadi pada untai FASSIP 03 NT selama komisioning. Hasil analisis dan perhitungan, laju aliran sirkulasi alam yang terbentuk adalah rejim aliran turbulen dengan rentang bilangan Reynolds (Re) dari 4305,8 – 7705,4. Dengan terjadinya aliran jenis turbulen pada untai FASSIP-03 NT berakibat permindahan panas yang terjadi menjadi lebih baik.
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 Ainur Rosidi Arno, Giarno Budi Suhendro Dedy Haryanto G. Bambang Heru K Istanto, Iwan Mulya Juarsa Rokhmat Arifianto Slamet Sulaiman , Robi Totok Dermawan Yadi Yunus