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Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification

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Abstract
Bearings are complex components with onlinear behavior that are used to mitigate the
effects of inertia. These components are used in various systems, including motors. Data analysis
and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore, a deep learning-based adaptive neural-fuzzy structure technique via a support vector autoregressive-Laguerre model is presented in this study. The proposed scheme has three main steps.
First, the support vector autoregressive-Laguerre is introduced to approximate the vibration signal
under normal conditions and extract the state-space equation. After signal modeling, an adaptive
neural-fuzzy structure observer is designed using a combination of high-order variable structure
techniques, the support vector autoregressive-Laguerre model, and adaptive neural-fuzzy inference
mechanism for normal and abnormal signal estimation. The adaptive neural-fuzzy structure observer is the main part of this work because, based on the difference between signal estimation accuracy, it can be used to identify faults in the bearings. Next, the residual signals are generated, and
the signal conditions are detected and identified using a convolution neural network (CNN) algorithm. The effectiveness of the proposed deep learning-based adaptive neural-fuzzy structure technique by support vector autoregressive-Laguerre model was analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed scheme is compared to five stateof-the-art techniques. The proposed algorithm improved the average pattern recognition and crack
size identification accuracy by 1.99%, 3.84%, 15.75%, 5.87%, 30.14%, and 35.29% compared to the
combination of the high-order variable structure technique with the support vector autoregressiveLaguerre model and CNN, the combination of the variable structure technique with the support
vector autoregressive-Laguerre model and CNN, the combination of RAW signal and CNN, the
combination of the adaptive neural-fuzzy structure technique with the support vector autoregressive-Laguerre model and support vector machine (SVM), the combination of the high-order variable
structure technique with the support vector autoregressive-Laguerre model and SVM, and the combination of the variable structure technique with the support vector autoregressive-Laguerre model
and SVM, respectively.
Author(s)
필탄 파르진즈엉 박 피김종면
Issued Date
2021
Type
Article
Keyword
adaptive neural-fuzzy techniqueautoregressive-Laguerre methodbearingconvolution neural networkcrack size identificationfault pattern recognitionhigh-order variable structure observersupport vector machinesupport vector regression technique
DOI
10.3390/s21062102
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9132
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_783b292622904faabb523923bc1dfb87&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Deep%20Learning-Based%20Adaptive%20Neural-Fuzzy%20Structure%20Scheme%20for%20Bearing%20Fault%20Pattern%20Recognition%20and%20Crack%20Size%20Identification&offset=0&pcAvailability=true
Publisher
SENSORS
Location
스위스
Language
영어
ISSN
1424-8220
Citation Volume
21
Citation Number
6
Citation Start Page
2102
Citation End Page
2102
Appears in Collections:
Engineering > IT Convergence
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