Prediction of Remaining Useful Life (RUL) in Refinery using Deep Learning
The Remaining Useful Life (RUL) is typically used as the process of predicting the life span of machine before its failure. The purpose of this project is to develop a predictive analysis system of Remaining Useful Life (RUL) in refinery. It will significantly help the oil and gas industry to...
| Main Author: | Baharadin, Hazirah |
|---|---|
| Format: | Final Year Project |
| Language: | English |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-utpedia.20901 / |
| Published: |
IRC
2019
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| Subjects: | |
| Online Access: |
http://utpedia.utp.edu.my/20901/1/Hazirah_22882.pdf http://utpedia.utp.edu.my/20901/ |
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| Summary: |
The Remaining Useful Life (RUL) is typically used as the process of predicting the
life span of machine before its failure. The purpose of this project is to develop a
predictive analysis system of Remaining Useful Life (RUL) in refinery. It will
significantly help the oil and gas industry to schedule a replacement or maintenance
before the machines end its operation. The project is developed using Deep Learning
algorithm which the functionality can be found in KNIME Analytic application. The
analysis dashboard by using Microsoft Power BI which will integrate with KNIME
Application, an artificial intelligence tool, will assist engineers to make informed
decision to replace or need maintenance at the end of its remaining useful life of the
machines |
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