A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory
Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 21(2021), 19 vom: 04. Okt. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gao, Yangde [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 14.10.2021 Date Revised 16.10.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.3390/s21196614 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM33181739X |
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| 100 | 1 | |a Gao, Yangde |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory |
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| 500 | |a Date Completed 14.10.2021 | ||
| 500 | |a Date Revised 16.10.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Extended Deep Convolutional Neural Networks | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a fault diagnosis | |
| 650 | 4 | |a long short-term memory | |
| 650 | 4 | |a rotating machinery | |
| 700 | 1 | |a Kim, Cheol Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Jong-Myon |e verfasserin |4 aut | |
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