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Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

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Abstract
This paper proposes an accurate and stable gearbox fault diagnosis scheme that combines a localized adaptive denoising technique with a wavelet-based vibration imaging approach and a deep convolution neural network model. Vibration signatures of a gearbox contain important fault-related information. However, this useful fault-related information is often overwhelmed by random interference noises. Furthermore, the varying speed of gearboxes makes it difficult to distinguish the fault-related frequencies from the interference noises. To obtain a noise-free signal for extraction of fault-related information under variable speed conditions, first, a new localized adaptive denoising technique (LADT) is applied to the vibration signal. The new localized adaptive denoising technique results in optimized vibration sub-bands with negligible background noise. To obtain fault-related information, the wavelet-based vibration imaging approach (WVI) is applied to the denoised vibration signal. The wavelet-based vibration imaging approach decomposes the vibration signal into different time-frequency scales, these scales are reflected by a two-dimensional image called a scalogram. The scalograms obtained from the wavelet-based vibration imaging approach are provided as an input to the deep convolutional neural network architecture (DCNA) for extraction of discriminant features and classification of multi-degree tooth faults (MDTFs) in a gearbox under variable speed conditions. The proposed scheme outperforms the already existing state-of-the-art gearbox fault diagnosis methods with the highest classification accuracy of 100%.
Author(s)
웬 꽁 다이아흐마드 자후르김종면
Issued Date
2021
Type
Article
Keyword
deep convolutional networkgearbox fault diagnosislocalized adaptive denoising
DOI
10.3390/app11167575
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9164
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_8daadbc03b0e4f50abfb9f53d7daf1f0&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Gearbox%20Fault%20Identification%20Framework%20Based%20on%20Novel%20Localized%20Adaptive%20Denoising%20Technique,%20Wavelet-Based%20Vibration%20Imaging,%20and%20Deep%20Convolutional%20Neural%20Network&offset=0&pcAvailability=true
Publisher
APPLIED SCIENCES-BASEL
Location
스위스
Language
영어
ISSN
2076-3417
Citation Volume
11
Citation Number
16
Citation Start Page
7575
Citation End Page
7575
Appears in Collections:
Engineering > IT Convergence
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