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Fault Diagnosis and Noise Robustness Comparison of Rotating Machinery using CWT and CNN

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Abstract
For systems using rotating machinery, diagnosing the faults of the rotating machinery is
critical for system maintenance. Recently, a machine learning algorithm has been employed
as one of the methods for diagnosing the faults of rotating machinery. This algorithm has
an advantage of automatically classifying faults without an expert knowledge. However,
despite a good training performance of the deep learning model, there remains a challenge
of performance degradation arising from noise when the model is applied in a real
environment. In this study, to solve this problem, we identified the faults of a rotating
machinery after applying the continuous wavelet transform (CWT) and then we extracted
the images for detecting the faults of rotating machinery and apply them to the convolution
neural network (CNN). Subsequently, we compared it with a commonly used artificial
neural network technique according to load and noise. When we added the white noise from
1dB to 20dB to vibration signal, the proposed method converged to 100% accuracy from
8dB at no load, at 10dB at presence of load. we verified that the proposed method improved
the performance in diagnosing the faults of rotating machinery.
Author(s)
김병우이종규
Issued Date
2021
Type
Article
Keyword
Rolling bearing fault diagnosisContinuous wavelet transformMachine learningDeep learningConvolution neural networkNoise Robustness
DOI
10.25046/aj0601146
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9012
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_crossref_primary_10_25046_aj0601146&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Fault%20Diagnosis%20and%20Noise%20Robustness%20Comparison%20of%20Rotating%20Machinery%20using%20CWT%20and%20CNN&offset=0&pcAvailability=true
Publisher
Advances in Science, Technology and Engineering Systems
Location
미국
Language
영어
ISSN
2415-6698
Citation Volume
6
Citation Number
1
Citation Start Page
1279
Citation End Page
1285
Appears in Collections:
Engineering > Electrical engineering
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