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A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction
Health assessment and remaining useful life prediction are usually seen as separate tasks in industrial systems. Some multitask models use common features to handle these tasks synchronously, but they lack the usage of the representation in different scales and time-frequency domain. A lack of balan...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967891/ https://www.ncbi.nlm.nih.gov/pubmed/36850519 http://dx.doi.org/10.3390/s23041922 |
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author | Wu, Tong Chen, Tengpeng |
author_facet | Wu, Tong Chen, Tengpeng |
author_sort | Wu, Tong |
collection | PubMed |
description | Health assessment and remaining useful life prediction are usually seen as separate tasks in industrial systems. Some multitask models use common features to handle these tasks synchronously, but they lack the usage of the representation in different scales and time-frequency domain. A lack of balance also exists among these scales. Therefore, a gated multiscale multitask learning model known as GMM-Net is proposed in this paper. By using the time-frequency representation, GMM-Net can obtain features of different scales via different kernels and compose the features by a gating network. A detailed loss function whose weight can be searched in a smaller scale is designed. The model is tested with different weights in the total loss function, and an optimal weight is found. Using this optimal weight, it is observed that the proposed method converges to a smaller loss and has a smaller model size than long short-term memory (LSTM) and gated recurrent unit (GRU) with less training time. The experiment results demonstrate the effectiveness of the proposed method. |
format | Online Article Text |
id | pubmed-9967891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99678912023-02-27 A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction Wu, Tong Chen, Tengpeng Sensors (Basel) Article Health assessment and remaining useful life prediction are usually seen as separate tasks in industrial systems. Some multitask models use common features to handle these tasks synchronously, but they lack the usage of the representation in different scales and time-frequency domain. A lack of balance also exists among these scales. Therefore, a gated multiscale multitask learning model known as GMM-Net is proposed in this paper. By using the time-frequency representation, GMM-Net can obtain features of different scales via different kernels and compose the features by a gating network. A detailed loss function whose weight can be searched in a smaller scale is designed. The model is tested with different weights in the total loss function, and an optimal weight is found. Using this optimal weight, it is observed that the proposed method converges to a smaller loss and has a smaller model size than long short-term memory (LSTM) and gated recurrent unit (GRU) with less training time. The experiment results demonstrate the effectiveness of the proposed method. MDPI 2023-02-08 /pmc/articles/PMC9967891/ /pubmed/36850519 http://dx.doi.org/10.3390/s23041922 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Tong Chen, Tengpeng A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction |
title | A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction |
title_full | A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction |
title_fullStr | A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction |
title_full_unstemmed | A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction |
title_short | A Gated Multiscale Multitask Learning Model Using Time-Frequency Representation for Health Assessment and Remaining Useful Life Prediction |
title_sort | gated multiscale multitask learning model using time-frequency representation for health assessment and remaining useful life prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967891/ https://www.ncbi.nlm.nih.gov/pubmed/36850519 http://dx.doi.org/10.3390/s23041922 |
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