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Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM
Aeroengine working condition recognition is a pivotal step in engine fault diagnosis. Currently, most research on aeroengine condition recognition focuses on the stable condition. To identify the aeroengine working conditions including transition conditions and better achieve the fault diagnosis of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503331/ https://www.ncbi.nlm.nih.gov/pubmed/36146420 http://dx.doi.org/10.3390/s22187071 |
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author | Zheng, Jinsong Peng, Jingbo Wang, Weixuan Li, Shuaiguo |
author_facet | Zheng, Jinsong Peng, Jingbo Wang, Weixuan Li, Shuaiguo |
author_sort | Zheng, Jinsong |
collection | PubMed |
description | Aeroengine working condition recognition is a pivotal step in engine fault diagnosis. Currently, most research on aeroengine condition recognition focuses on the stable condition. To identify the aeroengine working conditions including transition conditions and better achieve the fault diagnosis of engines, a recognition method based on the combination of multi-scale convolutional neural networks (MsCNNs) and bidirectional long short-term memory neural networks (BiLSTM) is proposed. Firstly, the MsCNN is used to extract the multi-scale features from the flight data. Subsequently, the spatial and channel weights are corrected using the weight adaptive correction module. Then, the BiLSTM is used to extract the temporal dependencies in the data. The Focal Loss is used as the loss function to improve the recognition ability of the model for confusable samples. L2 regularization and DropOut strategies are employed to prevent overfitting. Finally, the established model is used to identify the working conditions of an engine sortie, and the recognition results of different models are compared. The overall recognition accuracy of the proposed model reaches over 97%, and the recognition accuracy of transition conditions reaches 94%. The results show that the method based on MsCNN–BiLSTM can effectively identify the aeroengine working conditions including transition conditions accurately. |
format | Online Article Text |
id | pubmed-9503331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95033312022-09-24 Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM Zheng, Jinsong Peng, Jingbo Wang, Weixuan Li, Shuaiguo Sensors (Basel) Article Aeroengine working condition recognition is a pivotal step in engine fault diagnosis. Currently, most research on aeroengine condition recognition focuses on the stable condition. To identify the aeroengine working conditions including transition conditions and better achieve the fault diagnosis of engines, a recognition method based on the combination of multi-scale convolutional neural networks (MsCNNs) and bidirectional long short-term memory neural networks (BiLSTM) is proposed. Firstly, the MsCNN is used to extract the multi-scale features from the flight data. Subsequently, the spatial and channel weights are corrected using the weight adaptive correction module. Then, the BiLSTM is used to extract the temporal dependencies in the data. The Focal Loss is used as the loss function to improve the recognition ability of the model for confusable samples. L2 regularization and DropOut strategies are employed to prevent overfitting. Finally, the established model is used to identify the working conditions of an engine sortie, and the recognition results of different models are compared. The overall recognition accuracy of the proposed model reaches over 97%, and the recognition accuracy of transition conditions reaches 94%. The results show that the method based on MsCNN–BiLSTM can effectively identify the aeroengine working conditions including transition conditions accurately. MDPI 2022-09-19 /pmc/articles/PMC9503331/ /pubmed/36146420 http://dx.doi.org/10.3390/s22187071 Text en © 2022 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 Zheng, Jinsong Peng, Jingbo Wang, Weixuan Li, Shuaiguo Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM |
title | Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM |
title_full | Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM |
title_fullStr | Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM |
title_full_unstemmed | Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM |
title_short | Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM |
title_sort | aeroengine working condition recognition based on mscnn-bilstm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503331/ https://www.ncbi.nlm.nih.gov/pubmed/36146420 http://dx.doi.org/10.3390/s22187071 |
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