A Prediction Method for the RUL of Equipment for Missing Data

We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data...

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Main Authors: Chen Wenbai, Liu Chang, Chen Weizhao, Liu Huixiang, Chen Qili, Wu Peiliang
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2021/2122655
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author Chen Wenbai
Liu Chang
Chen Weizhao
Liu Huixiang
Chen Qili
Wu Peiliang
author_facet Chen Wenbai
Liu Chang
Chen Weizhao
Liu Huixiang
Chen Qili
Wu Peiliang
author_sort Chen Wenbai
collection DOAJ
description We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.
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id doaj-art-1d8b1c7ee93347ed9ed79fcec86d7998
institution Kabale University
issn 1099-0526
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-1d8b1c7ee93347ed9ed79fcec86d79982025-02-03T05:59:59ZengWileyComplexity1099-05262021-01-01202110.1155/2021/2122655A Prediction Method for the RUL of Equipment for Missing DataChen Wenbai0Liu Chang1Chen Weizhao2Liu Huixiang3Chen Qili4Wu Peiliang5School of AutomationNational Engineering Research Center for Information Technology in AgricultureSchool of AutomationSchool of AutomationSchool of AutomationSchool of Information Science & TechnologyWe present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.http://dx.doi.org/10.1155/2021/2122655
spellingShingle Chen Wenbai
Liu Chang
Chen Weizhao
Liu Huixiang
Chen Qili
Wu Peiliang
A Prediction Method for the RUL of Equipment for Missing Data
Complexity
title A Prediction Method for the RUL of Equipment for Missing Data
title_full A Prediction Method for the RUL of Equipment for Missing Data
title_fullStr A Prediction Method for the RUL of Equipment for Missing Data
title_full_unstemmed A Prediction Method for the RUL of Equipment for Missing Data
title_short A Prediction Method for the RUL of Equipment for Missing Data
title_sort prediction method for the rul of equipment for missing data
url http://dx.doi.org/10.1155/2021/2122655
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