Cargando…

Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network

Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge. However, due to ignoring the physical properties...

Descripción completa

Detalles Bibliográficos
Autores principales: Lu, Zhonghai, Guo, Chao, Liu, Mingrui, Shi, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287649/
https://www.ncbi.nlm.nih.gov/pubmed/37349382
http://dx.doi.org/10.1038/s41598-023-37154-5
_version_ 1785061917361963008
author Lu, Zhonghai
Guo, Chao
Liu, Mingrui
Shi, Rui
author_facet Lu, Zhonghai
Guo, Chao
Liu, Mingrui
Shi, Rui
author_sort Lu, Zhonghai
collection PubMed
description Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge. However, due to ignoring the physical properties of the target RUL function, neural networks can result in unreasonable RUL estimates such as going upwards and wrong endings. In the paper, we apply the fundamental principle of Physics-Informed Neural Network (PINN) to enhance Recurrent Neural Network (RNN) based RUL estimation methods. Through formulating proper constraints into the loss function of neural networks, we demonstrate in our experiments with the NASA IGBT dataset that PINN can make the neural networks trained more realistically and thus achieve performance improvements in estimation error and coefficient of determination. Compared to the baseline vanilla RNN, our physics-informed RNN can improve Mean Squared Error (MSE) of out-of-sample estimation on average by 24.7% in training and by 51.3% in testing; Compared to the baseline Long Short Term Memory (LSTM, a variant of RNN), our physics-informed LSTM can improve MSE of out-of-sample estimation on average by 15.3% in training and 13.9% in testing.
format Online
Article
Text
id pubmed-10287649
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-102876492023-06-24 Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network Lu, Zhonghai Guo, Chao Liu, Mingrui Shi, Rui Sci Rep Article Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge. However, due to ignoring the physical properties of the target RUL function, neural networks can result in unreasonable RUL estimates such as going upwards and wrong endings. In the paper, we apply the fundamental principle of Physics-Informed Neural Network (PINN) to enhance Recurrent Neural Network (RNN) based RUL estimation methods. Through formulating proper constraints into the loss function of neural networks, we demonstrate in our experiments with the NASA IGBT dataset that PINN can make the neural networks trained more realistically and thus achieve performance improvements in estimation error and coefficient of determination. Compared to the baseline vanilla RNN, our physics-informed RNN can improve Mean Squared Error (MSE) of out-of-sample estimation on average by 24.7% in training and by 51.3% in testing; Compared to the baseline Long Short Term Memory (LSTM, a variant of RNN), our physics-informed LSTM can improve MSE of out-of-sample estimation on average by 15.3% in training and 13.9% in testing. Nature Publishing Group UK 2023-06-22 /pmc/articles/PMC10287649/ /pubmed/37349382 http://dx.doi.org/10.1038/s41598-023-37154-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lu, Zhonghai
Guo, Chao
Liu, Mingrui
Shi, Rui
Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network
title Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network
title_full Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network
title_fullStr Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network
title_full_unstemmed Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network
title_short Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network
title_sort remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287649/
https://www.ncbi.nlm.nih.gov/pubmed/37349382
http://dx.doi.org/10.1038/s41598-023-37154-5
work_keys_str_mv AT luzhonghai remainingusefullifetimeestimationfordiscretepowerelectronicdevicesusingphysicsinformedneuralnetwork
AT guochao remainingusefullifetimeestimationfordiscretepowerelectronicdevicesusingphysicsinformedneuralnetwork
AT liumingrui remainingusefullifetimeestimationfordiscretepowerelectronicdevicesusingphysicsinformedneuralnetwork
AT shirui remainingusefullifetimeestimationfordiscretepowerelectronicdevicesusingphysicsinformedneuralnetwork