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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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Alternative Title
A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short?Term Memory
Abstract
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.
Author(s)
양덕고김철홍김종면
Issued Date
2021
Type
Article
Keyword
deep learningExtended Deep Convolutional Neural Networksfault diagnosislong short-term memoryrotating machinery
DOI
10.3390/s21196614
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9169
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_926c7c04bee040ae877da55a869a13a8&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,A%20Novel%20Hybrid%20Deep%20Learning%20Method%20for%20Fault%20Diagnosis%20of%20Rotating%20Machinery%20Based%20on%20Extended%20WDCNN%20and%20Long%20Short%3FTerm%20Memory&offset=0&pcAvailability=true
Publisher
SENSORS
Location
스위스
Language
영어
ISSN
1424-8220
Citation Volume
21
Citation Number
19
Citation Start Page
6614
Citation End Page
6614
Appears in Collections:
Engineering > IT Convergence
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