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: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/2122655 |
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| _version_ | 1832551932774842368 |
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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. |
| format | Article |
| 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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