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Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-based Deep Neural Network

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Abstract
Gearbox fault diagnosis based on the analysis of vibration signals has been a major re?
search topic for a few decades due to the advantages of vibration characteristics. Such characteristics
are used for early fault detection to guarantee the enhanced safety of complex systems and their
cost?effective operation. There exist many fault diagnosis models that have been developed for clas?
sifying various fault types in gearboxes. However, the classification results of the conventional fault
classification models degrade when they are applied to gearbox systems with multi?level tooth cut
gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in dis?
criminating the gearfault types. Due to the improved computational capabilities of modern systems,
the application of deep neural networks (DNNs) is getting popular in a variety of research fields,
such as image and natural language processing. DNNs are capable of improving the classification
results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this
research, an adaptive noise control (ANC) and a stacked sparse autoencoder?based deep neural
network (SSA?DNN) are used to construct a sensitive fault diagnosis model that can diagnose a
gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicat?
edness. An ANC is applied to gear vibration characteristics to remove a significant level of noise
along the frequency spectrum of vibration signals to fix the most fault?informative components of
each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault?
informative components to separate the fault types considered in this study. Furthermore, the im?
plementation of the SSA?DNN is substituted for feature extraction, feature selection, and the classi?
fication processes in traditional fault diagnosis schemes by high?performance unity. The experi?
mental results show that the proposed model outperforms conventional methodologies with higher
classification accuracy.
Author(s)
웬 꽁 다이프로스비린 알렉산데르김철홍김종면
Issued Date
2021
Type
Article
Keyword
adaptive noise reducerGaussian reference signalgearbox fault diagnosisstacked sparse autoencoder–based deep neural networkvarying rotational speed
DOI
10.3390/s21010018
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9035
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_0655be0ec3ba4534af97edb245871774&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Construction%20of%20a%20Sensitive%20and%20Speed%20Invariant%20Gearbox%20Fault%20Diagnosis%20Model%20Using%20an%20Incorporated%20Utilizing%20Adaptive%20Noise%20Control%20and%20a%20Stacked%20Sparse%20Autoencoder-based%20Deep%20Neural%20Network&offset=0&pcAvailability=true
Publisher
SENSORS
Location
스위스
Language
영어
ISSN
1424-8220
Citation Volume
21
Citation Number
1
Citation Start Page
18
Citation End Page
18
Appears in Collections:
Engineering > IT Convergence
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