Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset

There are many types of equipment involved in the oil and gas industry. However, they have their useful lives and will degrade over time. This issue prompts to be solved using predictive analytics to predict the Remaining Useful Life (RUL) of equipment. In the historical data, however, there are mis...

Full description

Main Authors: Amirruddin, A., Aziz, I.A., Hasan, M.H.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23078 /
Published: World Academy of Research in Science and Engineering 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092635857&doi=10.30534%2fijatcse%2f2020%2f39952020&partnerID=40&md5=0fd9441e4ce8c24c95bc1abb20e30d9a
http://eprints.utp.edu.my/23078/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.23078
recordtype eprints
spelling utp-eprints.230782021-08-19T05:27:35Z Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset Amirruddin, A. Aziz, I.A. Hasan, M.H. There are many types of equipment involved in the oil and gas industry. However, they have their useful lives and will degrade over time. This issue prompts to be solved using predictive analytics to predict the Remaining Useful Life (RUL) of equipment. In the historical data, however, there are missing values due to broken equipment sensors probes and different time rate sensors. This can significantly affect the prediction results and making it less accurate due to missing value and become a challenging issue. Missing values in datasets is a synonymous problem in data mining which could lead to an incomplete dataset, making inaccurate predictions results in machine learning prediction processes. This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches. SVR however is inferior in accuracy and thus this paper discusses the usage of an optimized SVR with Evolved Bat Algorithm (EBA) to handle the missing value accurately with high execution time. The paper also presents the topic of missing values in the dataset, as well as compares the performance of the optimized SVR with the original SVR in terms of accuracy and execution time while handling missing values in a large dataset. The novel optimization-based artificial intelligence algorithm proposed in this paper implies an improved way to overcome a real engineering challenge i.e. handling missing values for better RUL prediction, hence bringing great opportunities for the domain area. © 2020, World Academy of Research in Science and Engineering. All rights reserved. World Academy of Research in Science and Engineering 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092635857&doi=10.30534%2fijatcse%2f2020%2f39952020&partnerID=40&md5=0fd9441e4ce8c24c95bc1abb20e30d9a Amirruddin, A. and Aziz, I.A. and Hasan, M.H. (2020) Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset. International Journal of Advanced Trends in Computer Science and Engineering, 9 (5). pp. 7157-7164. http://eprints.utp.edu.my/23078/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description There are many types of equipment involved in the oil and gas industry. However, they have their useful lives and will degrade over time. This issue prompts to be solved using predictive analytics to predict the Remaining Useful Life (RUL) of equipment. In the historical data, however, there are missing values due to broken equipment sensors probes and different time rate sensors. This can significantly affect the prediction results and making it less accurate due to missing value and become a challenging issue. Missing values in datasets is a synonymous problem in data mining which could lead to an incomplete dataset, making inaccurate predictions results in machine learning prediction processes. This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches. SVR however is inferior in accuracy and thus this paper discusses the usage of an optimized SVR with Evolved Bat Algorithm (EBA) to handle the missing value accurately with high execution time. The paper also presents the topic of missing values in the dataset, as well as compares the performance of the optimized SVR with the original SVR in terms of accuracy and execution time while handling missing values in a large dataset. The novel optimization-based artificial intelligence algorithm proposed in this paper implies an improved way to overcome a real engineering challenge i.e. handling missing values for better RUL prediction, hence bringing great opportunities for the domain area. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
format Article
author Amirruddin, A.
Aziz, I.A.
Hasan, M.H.
spellingShingle Amirruddin, A.
Aziz, I.A.
Hasan, M.H.
Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
author_sort Amirruddin, A.
title Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
title_short Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
title_full Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
title_fullStr Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
title_full_unstemmed Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
title_sort auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
publisher World Academy of Research in Science and Engineering
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092635857&doi=10.30534%2fijatcse%2f2020%2f39952020&partnerID=40&md5=0fd9441e4ce8c24c95bc1abb20e30d9a
http://eprints.utp.edu.my/23078/
_version_ 1741196617166356480
score 11.62408

Similar Items