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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) CommIT (Communication & Information Technology) Jurnal CoreIT Jurnal Penelitian Pendidikan IPA (JPPIPA) JTERA (Jurnal Teknologi Rekayasa) SINTECH (Science and Information Technology) Journal JISA (Jurnal Informatika dan Sains) Jurnal Pengabdian kepada Masyarakat Bina Darma IT For Society : Journal of Information Technology Jurnal Manajemen Sistem Informasi
Rusdianto Roestam
President University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Physics Other

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Journal : JISA (Jurnal Informatika dan Sains)

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Online Monitoring and Analysis of Lube Oil Degradation for Gas Turbine Engine using Recurrent Neural Network (RNN) Febrianto Nugroho; Rusdianto Roestam
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1108

Abstract

Lubrication is one of the important aspects of the engine that will impact the overall performance of the gas turbine engine. Degradation of oil is usually known by offline analysis that use oil sample to check some properties and contaminant. The offline analysis will take a longer time, as needed to collect the sample, send it to the laboratory, analyze the sample and create the report. The purpose of this research is to analyze oil parameters in real-time so can predict oil degradation. Sensors and transducers installed on the lube oil system can read some parameters of the oil then transmit easily to the server. The method that will use in this paper is Recurrent Neural Network (RNN) with multi-step Long Short Term Memory (LSTM). The result of this paper will predict oil degradation on the future operation of gas turbine engine.
Co-Authors Abdusy Syarif Afrina Afrina Afrizal Nehemia Toscany Alinda Endang Poerwati Amadeuz Ezrafel Amir Dahlan Ayu Nur Indahsari Ayu Nur Indahsari Dedi Rianto Rahadi Dodi Setiawan Dwi Utami, Rina Febrianto Nugroho Hadi Suprayitno Hantoro, Kusdarnowo Irham Hernando Koo Tito Novelianto Lila Setiyani Lila Setiyani Liliani Priestiwanty Lita Yusnita Muhammad Daffa Akbari Oka Ediansa R. B. Wahyu Rendy Winata Roby Setiawan Rosalina Rosalina Ryandra Anditto Sella Monica Suwarto Suwarto Suyanti Suyanti Trisnawati Gulo Yuliarman Saragih