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Gearbox Fault Diagnosis Under Variable Speed Condition Using Frequency Spectral Analysis with 1D Residual Neural Network

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Abstract
Stringent classification of gearbox fault conditions is indispensable for
industrial safety. Determining the best set of features by analyzing the statistical
parameters of the signals is one of themost climacteric tasks in data-driven fault diagnosis.
For variable speed conditions, variant fault types of gearboxes have dynamic
characteristics and these statistical features fail to unveil them. To address this issue,
deep learning algorithms are used to produce a better performance of the feature
selection process. In this paper, a combination of frequency spectral analysis of the
acoustic emission signals and a 1-dimensional residual neural network (1D-RNN)
is proposed. Our proposed method through the processed 1D-RNN shows vigorous
classification performance, resulting in up to 95.6% classification accuracy in all the
considered scenarios.
Author(s)
하비브 엠디 아라파트김종면
Issued Date
2021
Type
Article
Keyword
Gearbox safetyFault diagnosisConvolutional neural network
DOI
10.1007/978-981-15-9343-7_31
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9127
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_981_15_9343_7_31&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Gearbox%20Fault%20Diagnosis%20Under%20Variable%20Speed%20Condition%20Using%20Frequency%20Spectral%20Analysis%20with%201D%20Residual%20Neural%20Network&offset=0&pcAvailability=true
Publisher
Lecture Notes in Electrical Engineering
Location
독일
Language
영어
ISSN
1876-1100
Citation Volume
715
Citation Number
1
Citation Start Page
227
Citation End Page
233
Appears in Collections:
Engineering > IT Convergence
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