DETEKSI ANOMALI PADA SINYAL VIBRASI BERBASIS VARIATIONAL AUTOENCODER

UMARI, ZAINAL and Supardi, Julian (2024) DETEKSI ANOMALI PADA SINYAL VIBRASI BERBASIS VARIATIONAL AUTOENCODER. Masters thesis, Sriwijaya University.

Preview	Image RAMA_55101_09012682327002_cover.jpg - Cover Image Available under License Creative Commons Public Domain Dedication. Download (351kB) | Preview
	Text RAMA_55101_09012682327002.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (11MB) | Request a copy
	Text RAMA_55101_09012682327002_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_55101_09012682327002_0010077210_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB)
	Text RAMA_55101_09012682327002_0010077210_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_55101_09012682327002_0010077210_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_55101_09012682327002_0010077210_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_55101_09012682327002_0010077210_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (100kB) | Request a copy
	Text RAMA_55101_09012682327002_0010077210_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (114kB) | Request a copy
	Text RAMA_55101_09012682327002_0010077210_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy

Abstract

Analisis sinyal vibrasi telah lama diandalkan dalam berbagai industri sebagai metode untuk mendeteksi anomali yang berpotensi menyebabkan kerusakan serius. Namun, tingginya resolusi data dan ketiadaan label membuat deteksi anomali pada sinyal vibrasi sulit dilakukan dengan metode tradisional. Penelitian ini bertujuan untuk mengembangkan model Variational Autoencoder (VAE) yang mampu mendeteksi anomali pada sinyal vibrasi beresolusi tinggi tanpa memerlukan pelabelan data. Data yang digunakan diperoleh dari tiga unit mesin blower identik di PT. Pusri Palembang. Hasil penelitian menunjukkan bahwa model VAE yang dikembangkan mampu mendeteksi anomali dengan akurasi tinggi, menjadikannya solusi yang andal dalam pemantauan kondisi mesin. Penelitian ini menawarkan pendekatan praktis bagi industri untuk meningkatkan efisiensi pemeliharaan dan keandalan peralatan.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	analisis sinyal vibrasi, deteksi anomali, pembelajaran tanpa pengawasan, variational autoencoder
Subjects:	T Technology > T Technology (General) > T57-57.97 Applied mathematics. Quantitative methods > T57.5 Data processing Cf. HF5548.125+ Business data processing Operations research. Systems analysis
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Zainal Umari
Date Deposited:	10 Jan 2025 04:15
Last Modified:	10 Jan 2025 04:15
URI:	http://repository.unsri.ac.id/id/eprint/163454

Actions (login required)

View Item