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Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components

Accurate remaining useful life (RUL) analysis of a machinery system is of great importance. Such systems work in long-term operations in which unexpected failures often occur. Due to the rapid development of computer technology, the deep learning model has supplanted physical-based RUL analysis. The...

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Main Authors: Mohd. Saufi, M. S. R., Hassan, K. A.
Format: Article
Published: Elsevier Ltd. 2021
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/id/eprint/94973/
http://dx.doi.org/10.1016/j.asoc.2021.107817
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spelling my.utm.949732022-04-29T22:00:13Z http://eprints.utm.my/id/eprint/94973/ Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components Mohd. Saufi, M. S. R. Hassan, K. A. TJ Mechanical engineering and machinery Accurate remaining useful life (RUL) analysis of a machinery system is of great importance. Such systems work in long-term operations in which unexpected failures often occur. Due to the rapid development of computer technology, the deep learning model has supplanted physical-based RUL analysis. The data-driven approach using the deep learning model is capable of providing an accurate RUL analysis. However, an accurate analysis using deep learning comes with challenges and costs. In current RUL analysis practice, the deep learning hyperparameters are manually selected, which hinders the deep network from reaching the local optima. Additionally, current practice uses powerful signal processing methods with complicated prediction indicators. Therefore, a novel methodological step is proposed to tackle this problem by integrating the Laplacian score (LS), random search optimisation and long short-term memory (LSTM). The proposed system, called integrated Laplacian-LSTM, produced accurate RUL analyses on the IEEE PHM 2012 Competition and IMS bearing datasets, showing significant improvement in prediction accuracy. This system increases prediction accuracy by 18% compared to other available RUL methods in similar studies. Elsevier Ltd. 2021 Article PeerReviewed Mohd. Saufi, M. S. R. and Hassan, K. A. (2021) Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components. Applied Soft Computing, 112 . ISSN 1568-4946 http://dx.doi.org/10.1016/j.asoc.2021.107817 DOI: 10.1016/j.asoc.2021.107817
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Mohd. Saufi, M. S. R.
Hassan, K. A.
Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components
description Accurate remaining useful life (RUL) analysis of a machinery system is of great importance. Such systems work in long-term operations in which unexpected failures often occur. Due to the rapid development of computer technology, the deep learning model has supplanted physical-based RUL analysis. The data-driven approach using the deep learning model is capable of providing an accurate RUL analysis. However, an accurate analysis using deep learning comes with challenges and costs. In current RUL analysis practice, the deep learning hyperparameters are manually selected, which hinders the deep network from reaching the local optima. Additionally, current practice uses powerful signal processing methods with complicated prediction indicators. Therefore, a novel methodological step is proposed to tackle this problem by integrating the Laplacian score (LS), random search optimisation and long short-term memory (LSTM). The proposed system, called integrated Laplacian-LSTM, produced accurate RUL analyses on the IEEE PHM 2012 Competition and IMS bearing datasets, showing significant improvement in prediction accuracy. This system increases prediction accuracy by 18% compared to other available RUL methods in similar studies.
format Article
author Mohd. Saufi, M. S. R.
Hassan, K. A.
author_facet Mohd. Saufi, M. S. R.
Hassan, K. A.
author_sort Mohd. Saufi, M. S. R.
title Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components
title_short Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components
title_full Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components
title_fullStr Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components
title_full_unstemmed Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components
title_sort remaining useful life prediction using an integrated laplacian-lst< network on machinery components
publisher Elsevier Ltd.
publishDate 2021
url http://eprints.utm.my/id/eprint/94973/
http://dx.doi.org/10.1016/j.asoc.2021.107817
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score 13.211869

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