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Self-Tuning Intelligence Digital Twin for Bearing Pattern Recognition

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Abstract
Induction motors are consumed around 80% of energy in heavy industries,
that approximately 20% of this energy consumption is because ofmechanical
failures. Moreover, the bearing failure with about 69% is the principal constituent
of mechanical defects. In this study, the self-tuning intelligence digital twin is
presented for bearing pattern recognition. The self-tuning digital twin is designed
using the combination of knowledge-based models, physical models, and linear
dynamic Bayesian estimator. In the physical-based approach, the mathematicalbased
vibration signal modeling is used. To reduce the effect of unknown states, the
knowledge-based technique based on an adaptive network-based fuzzy algorithm
is recommended. To evaluate the signal estimation, the linear dynamic Bayesian
estimation using the proportional-integral technique is endorsed. To classification,
the bearing signals and pattern recognition the machine learning approach
is recommended. Regarding the results, the bearing signals pattern recognition
accuracy is 98%.
Author(s)
필탄 파르진김종면
Issued Date
2021
Type
Article
Keyword
Adaptive network-based fuzzyDigital twinDynamic Bayesian estimationInduction motorMachine learning clusterMathematical-based vibration signal modelingPattern recognition
DOI
10.1007/978-3-030-85626-7_7
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9081
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_3_030_85626_7_7&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Self-Tuning%20Intelligence%20Digital%20Twin%20for%20Bearing%20Pattern%20Recognition&offset=0&pcAvailability=true
Publisher
Lecture Notes in Networks and Systems
Location
스위스
Language
영어
ISSN
2367-3370
Citation Volume
307
Citation Number
1
Citation Start Page
52
Citation End Page
59
Appears in Collections:
Engineering > IT Convergence
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