eprintid: 12628
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/26/28
datestamp: 2023-11-10 03:27:11
lastmod: 2023-11-10 03:27:11
status_changed: 2023-11-10 01:49:08
type: conference_item
metadata_visibility: show
creators_name: Aziz, N.
creators_name: Akhir, E.A.P.
creators_name: Aziz, I.A.
creators_name: Jaafar, J.
creators_name: Hasan, M.H.
creators_name: Abas, A.N.C.
title: A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
ispublished: pub
keywords: Intelligent computing; Machine learning; Predictive maintenance, Gradient boosting; High confidence; Machine failure; Machine learning methods; Monitoring tasks; Potential systems; Prediction systems; Test machine, Forecasting
note: cited By 7; Conference of 2020 International Conference on Computational Intelligence, ICCI 2020 ; Conference Date: 8 October 2020 Through 9 October 2020; Conference Code:164916
abstract: 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.
date: 2020
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097554112&doi=10.1109%2fICCI51257.2020.9247843&partnerID=40&md5=ce83980248934b97b06515083af2e6c1
id_number: 10.1109/ICCI51257.2020.9247843
full_text_status: none
publication: 2020 International Conference on Computational Intelligence, ICCI 2020
pagerange: 11-16
refereed: TRUE
isbn: 9781728154473
citation:   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.