A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems

Data-driven predictive maintenance for the prediction of machine failure has been widely studied and performed to test machine failures. Predictive maintenance refers to the machine learning method, which utilizes data for identification of potential system malfunction and provides an alert when a s...

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Main Authors: Aziz, N., Akhir, E.A.P., Aziz, I.A., Jaafar, J., Hasan, M.H., Abas, A.N.C.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29890 /
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097554112&doi=10.1109%2fICCI51257.2020.9247843&partnerID=40&md5=ce83980248934b97b06515083af2e6c1
http://eprints.utp.edu.my/29890/
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id utp-eprints.29890
recordtype eprints
spelling utp-eprints.298902022-03-25T03:05:32Z A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems Aziz, N. Akhir, E.A.P. Aziz, I.A. Jaafar, J. Hasan, M.H. Abas, A.N.C. Data-driven predictive maintenance for the prediction of machine failure has been widely studied and performed to test machine failures. Predictive maintenance refers to the machine learning method, which utilizes data for identification of potential system malfunction and provides an alert when a system assessed to be prone to breakdown. The proposed work reveals a novel framework called Artificial Intelligence Monitoring 4.0 (AIM 4.0), which is capable of determining the current condition of equipment and provide a predicted mean time before failure occurs. AIM 4.0 utilizes three different ensemble machine learning methods, including Gradient Boost Machine (GBM), Light GBM, and XGBoost for prediction of machine failures. The machine learning methods stated are implemented to produce acceptable accuracy for the monitoring task as well as producing a prediction with a high confidence level. © 2020 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097554112&doi=10.1109%2fICCI51257.2020.9247843&partnerID=40&md5=ce83980248934b97b06515083af2e6c1 Aziz, N. and Akhir, E.A.P. and Aziz, I.A. and Jaafar, J. and Hasan, M.H. and Abas, A.N.C. (2020) A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems. In: UNSPECIFIED. http://eprints.utp.edu.my/29890/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Data-driven predictive maintenance for the prediction of machine failure has been widely studied and performed to test machine failures. Predictive maintenance refers to the machine learning method, which utilizes data for identification of potential system malfunction and provides an alert when a system assessed to be prone to breakdown. The proposed work reveals a novel framework called Artificial Intelligence Monitoring 4.0 (AIM 4.0), which is capable of determining the current condition of equipment and provide a predicted mean time before failure occurs. AIM 4.0 utilizes three different ensemble machine learning methods, including Gradient Boost Machine (GBM), Light GBM, and XGBoost for prediction of machine failures. The machine learning methods stated are implemented to produce acceptable accuracy for the monitoring task as well as producing a prediction with a high confidence level. © 2020 IEEE.
format Conference or Workshop Item
author Aziz, N.
Akhir, E.A.P.
Aziz, I.A.
Jaafar, J.
Hasan, M.H.
Abas, A.N.C.
spellingShingle Aziz, N.
Akhir, E.A.P.
Aziz, I.A.
Jaafar, J.
Hasan, M.H.
Abas, A.N.C.
A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
author_sort Aziz, N.
title A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
title_short A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
title_full A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
title_fullStr A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
title_full_unstemmed A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
title_sort study on gradient boosting algorithms for development of ai monitoring and prediction systems
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097554112&doi=10.1109%2fICCI51257.2020.9247843&partnerID=40&md5=ce83980248934b97b06515083af2e6c1
http://eprints.utp.edu.my/29890/
_version_ 1741197315471835136
score 11.62408

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