Development of Advanced Predictive Maintenance System

Unplanned or unnecessary maintenance of the equipment and instruments leads to waste of money and time in production or manufacturing plants. Current predictive maintenance techniques make use of offline data and basic prognostics tools that provide insufficiently accurate predictions. Moreover, exi...

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Main Author: Babakulyyev, Rustam
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.20133 /
Published: IRC 2019
Online Access: http://utpedia.utp.edu.my/20133/1/Final%20Dissertation.pdf
http://utpedia.utp.edu.my/20133/
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id utp-utpedia.20133
recordtype eprints
spelling utp-utpedia.201332021-10-29T12:04:47Z http://utpedia.utp.edu.my/20133/ Development of Advanced Predictive Maintenance System Babakulyyev, Rustam Unplanned or unnecessary maintenance of the equipment and instruments leads to waste of money and time in production or manufacturing plants. Current predictive maintenance techniques make use of offline data and basic prognostics tools that provide insufficiently accurate predictions. Moreover, existing predictive maintenance systems developed by external vendors are only accessible by large firms due to their high price. This project was initiated to develop an online, open-source and relatively accurate predictive maintenance system that employs Autoregressive Moving Average (ARMA) statistical prognostics method for small-sized companies that aim to supply products with the least waste of time and money in the process. In this project, the proposed system was applied on 5 cases including current, voltage, active power, cold air temperature and discharge pressure of a process plant. IRC 2019 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20133/1/Final%20Dissertation.pdf Babakulyyev, Rustam (2019) Development of Advanced Predictive Maintenance System. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
description Unplanned or unnecessary maintenance of the equipment and instruments leads to waste of money and time in production or manufacturing plants. Current predictive maintenance techniques make use of offline data and basic prognostics tools that provide insufficiently accurate predictions. Moreover, existing predictive maintenance systems developed by external vendors are only accessible by large firms due to their high price. This project was initiated to develop an online, open-source and relatively accurate predictive maintenance system that employs Autoregressive Moving Average (ARMA) statistical prognostics method for small-sized companies that aim to supply products with the least waste of time and money in the process. In this project, the proposed system was applied on 5 cases including current, voltage, active power, cold air temperature and discharge pressure of a process plant.
format Final Year Project
author Babakulyyev, Rustam
spellingShingle Babakulyyev, Rustam
Development of Advanced Predictive Maintenance System
author_sort Babakulyyev, Rustam
title Development of Advanced Predictive Maintenance System
title_short Development of Advanced Predictive Maintenance System
title_full Development of Advanced Predictive Maintenance System
title_fullStr Development of Advanced Predictive Maintenance System
title_full_unstemmed Development of Advanced Predictive Maintenance System
title_sort development of advanced predictive maintenance system
publisher IRC
publishDate 2019
url http://utpedia.utp.edu.my/20133/1/Final%20Dissertation.pdf
http://utpedia.utp.edu.my/20133/
_version_ 1741195581415489536
score 11.62408

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