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Prediction of Machine Failure by Using Machine Learning Algorithm

Machine failure halt many processes and causes minimum usage of unexploited resources. Prediction of the anomalies of a machine can act as an indicator and precaution to avoid machine malfunction. Prior to that, the big data undergo preprocessing; data transpose and imputation. Then, the data...

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Main Author: Fakhrurazi, Nur Amalina
Format: Final Year Project
Language:English
Published: IRC 2019
Subjects:
Q Science (General)
Online Access:http://utpedia.utp.edu.my/20846/1/NurAmalinaFakhrurazi_24184.pdf
http://utpedia.utp.edu.my/20846/
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spelling my-utp-utpedia.208462021-09-09T14:19:06Z http://utpedia.utp.edu.my/20846/ Prediction of Machine Failure by Using Machine Learning Algorithm Fakhrurazi, Nur Amalina Q Science (General) Machine failure halt many processes and causes minimum usage of unexploited resources. Prediction of the anomalies of a machine can act as an indicator and precaution to avoid machine malfunction. Prior to that, the big data undergo preprocessing; data transpose and imputation. Then, the data is cluster by using K Means to produce labeled input that will be trained by using Gradient Boosting Machine, a decision tree algorithm to make prediction. The columns consist of the variables that record the reading of machine sensor tags. Validation for the model is analyzed by using validation testing data and cross validation. Model built resulted in variables importance’s ranking and subsequently, prediction can be made. The results of the data analysis will be illustrated in a dashboard via Power BI. Consequently, the user will be able to make an informed decision. IRC 2019-05 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20846/1/NurAmalinaFakhrurazi_24184.pdf Fakhrurazi, Nur Amalina (2019) Prediction of Machine Failure by Using Machine Learning Algorithm. IRC, Universiti Teknologi PETRONAS. (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Fakhrurazi, Nur Amalina
Prediction of Machine Failure by Using Machine Learning Algorithm
description Machine failure halt many processes and causes minimum usage of unexploited resources. Prediction of the anomalies of a machine can act as an indicator and precaution to avoid machine malfunction. Prior to that, the big data undergo preprocessing; data transpose and imputation. Then, the data is cluster by using K Means to produce labeled input that will be trained by using Gradient Boosting Machine, a decision tree algorithm to make prediction. The columns consist of the variables that record the reading of machine sensor tags. Validation for the model is analyzed by using validation testing data and cross validation. Model built resulted in variables importance’s ranking and subsequently, prediction can be made. The results of the data analysis will be illustrated in a dashboard via Power BI. Consequently, the user will be able to make an informed decision.
format Final Year Project
author Fakhrurazi, Nur Amalina
author_facet Fakhrurazi, Nur Amalina
author_sort Fakhrurazi, Nur Amalina
title Prediction of Machine Failure by Using Machine Learning Algorithm
title_short Prediction of Machine Failure by Using Machine Learning Algorithm
title_full Prediction of Machine Failure by Using Machine Learning Algorithm
title_fullStr Prediction of Machine Failure by Using Machine Learning Algorithm
title_full_unstemmed Prediction of Machine Failure by Using Machine Learning Algorithm
title_sort prediction of machine failure by using machine learning algorithm
publisher IRC
publishDate 2019
url http://utpedia.utp.edu.my/20846/1/NurAmalinaFakhrurazi_24184.pdf
http://utpedia.utp.edu.my/20846/
_version_ 1739832802870820864
score 13.211869

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