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Hybrid Rubbing Fault Identification Using a Deep Learning-Based Observation Technique

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Abstract
A rub-impact fault is a complex, nonstationary, and
nonlinear fault that occurs in turbines. Extracting features for
diagnosing rubbing faults at their early stages requires complex
and computationally expensive signal processing approaches that
are not always suitable for industrial applications. In this article,
a hybrid approach that uses a combination of deep learning
and control theory algorithms is introduced for diagnosing
rubbing faults of various intensities. Specifically, the system
is first modeled based on the autoregressive with eXogenous
input Laguerre (ARX-Laguerre) technique. In addition, the
ARX-Laguerre proportional-integral observer (PIO) is used to
increase the estimation accuracy for the vibration signals containing
rubbing faults. Finally, a scalable deep neural network
is applied to the output signal of the PIO to perform fault
diagnosis and overcome potential problems that may appear
when applying a linear observation technique to nonlinear
signals. The experimental results demonstrate that the proposed
hybrid approach improves the fault differentiation capabilities of
a relatively simple linear observation technique when it is applied
to a complex nonlinear rubbing fault signal and attains high
fault classification accuracy. This result means that the proposed
framework is highly suitable for applications in actual industrial
environments.
Author(s)
프로스비린 알렉산데르필탄 파르진김종면
Issued Date
2021
Type
Article
Keyword
ARX-Laguerre techniqueAutoregressive processesAutoregressive with eXogenous input Laguerre (ARX-Laguerre) proportional-integral observer (PIO)Deep learningdeep neural networks (DNNs)Fault diagnosisFeature extractionNeural networksObserversrub-impact faultscalable deep neural network (S-DNN)
DOI
10.1109/TNNLS.2020.3027160
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9177
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2449955716&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Hybrid%20Rubbing%20Fault%20Identification%20Using%20a%20Deep%20Learning-Based%20Observation%20Technique&offset=0&pcAvailability=true
Publisher
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Location
미국
Language
영어
ISSN
2162-237X
Citation Volume
32
Citation Number
11
Citation Start Page
5144
Citation End Page
5154
Appears in Collections:
Engineering > IT Convergence
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