Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment

Today, modern industrial equipment is very complex as it involves sophisticated assets and systems. Thus, machine equipment optimization and safety have become operators' main concerns in the quest for maintaining optimum operational efficiency, asset availability, safety and cost-effecti...

Full description

Main Author: Mohd Fauzi, Muhammad Farhan Asyraf
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.20925 /
Published: IRC 2019
Subjects:
Q Science (General)
Online Access: http://utpedia.utp.edu.my/20925/1/Muhammad%20Farhan%20Asyraf%20Mohd%20Fauzi_22963.pdf
http://utpedia.utp.edu.my/20925/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-utpedia.20925
recordtype eprints
spelling utp-utpedia.209252021-09-09T20:18:00Z http://utpedia.utp.edu.my/20925/ Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment Mohd Fauzi, Muhammad Farhan Asyraf Q Science (General) Today, modern industrial equipment is very complex as it involves sophisticated assets and systems. Thus, machine equipment optimization and safety have become operators' main concerns in the quest for maintaining optimum operational efficiency, asset availability, safety and cost-effective. Due to its complexity of the internal structure of the equipment, engineers are often faced with large amounts of information called multivariate datasets which are hard to understand by human nature. This led to difficulty in achieving high accuracy prediction of the equipment and decision-making is hard to achieve. Thus, an organization unable to decide whether to purchase new equipment or provide maintenance strategies. Hence, the purpose of this research is to develop a machine learning workflow model of the integration between Alteryx tools to do prediction of RUL using “real world” multivariate dataset in Oil and Gas industry, and Microsoft Power BI to visualize the result of the prediction for a better insight. One of the most popular machine learning approaches is employed in the prediction workflow which is the Artificial Neural Network (ANN) algorithm, due to its capability to learn from a large volume of data points and high prediction accuracy. Performances of the accuracy of prediction workflow were measured using root mean squared error (RMSE). IRC 2019-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20925/1/Muhammad%20Farhan%20Asyraf%20Mohd%20Fauzi_22963.pdf Mohd Fauzi, Muhammad Farhan Asyraf (2019) Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic Q Science (General)
spellingShingle Q Science (General)
Mohd Fauzi, Muhammad Farhan Asyraf
Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment
description Today, modern industrial equipment is very complex as it involves sophisticated assets and systems. Thus, machine equipment optimization and safety have become operators' main concerns in the quest for maintaining optimum operational efficiency, asset availability, safety and cost-effective. Due to its complexity of the internal structure of the equipment, engineers are often faced with large amounts of information called multivariate datasets which are hard to understand by human nature. This led to difficulty in achieving high accuracy prediction of the equipment and decision-making is hard to achieve. Thus, an organization unable to decide whether to purchase new equipment or provide maintenance strategies. Hence, the purpose of this research is to develop a machine learning workflow model of the integration between Alteryx tools to do prediction of RUL using “real world” multivariate dataset in Oil and Gas industry, and Microsoft Power BI to visualize the result of the prediction for a better insight. One of the most popular machine learning approaches is employed in the prediction workflow which is the Artificial Neural Network (ANN) algorithm, due to its capability to learn from a large volume of data points and high prediction accuracy. Performances of the accuracy of prediction workflow were measured using root mean squared error (RMSE).
format Final Year Project
author Mohd Fauzi, Muhammad Farhan Asyraf
author_sort Mohd Fauzi, Muhammad Farhan Asyraf
title Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment
title_short Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment
title_full Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment
title_fullStr Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment
title_full_unstemmed Machine Learning Workflow to Predict Remaining Useful Life (RUL) of Equipment
title_sort machine learning workflow to predict remaining useful life (rul) of equipment
publisher IRC
publishDate 2019
url http://utpedia.utp.edu.my/20925/1/Muhammad%20Farhan%20Asyraf%20Mohd%20Fauzi_22963.pdf
http://utpedia.utp.edu.my/20925/
_version_ 1741195683228024832
score 11.62408

Similar Items