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Induction Motor Bearing Fault Diagnosis Using Statistical Time Domain Features and Hypertuning of Classifiers

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Abstract
Condition monitoring of induction motors plays a significant role in
avoiding unexpected breakdowns and reducing excessive maintenance costs. In the
majority of cases, bearing faults are found to be an issue in the failure of induction
motors. The detection and valuation of irregularities at an early stage can help prevent
disastrous failures. In this paper, the detection and classification of bearing faults in
an induction motor are performed using machine learning techniques. The current
signal from two different phases is recorded for three motor conditions: healthy, inner
race fault and outer race fault. The statistical features are then applied for dimensionality
reduction. Finally, the statistical features are used as the input of classifiers,
including support vector machines (SVMs), random forests (RFs), and k-nearest
neighbor (KNN). The grid search method is used to estimate the best-suited metaparameters
for each classifier to achieve the best performance in fault classification.
With the regularization parameters, all the classifiers achieve over 98% classification
accuracy.
Author(s)
토마 라피아 니샤트김종면
Issued Date
2021
Type
Article
Keyword
Induction motorBearing fault diagnosisStatistical featuresClassifiersGrid search method
DOI
10.1007/978-981-15-9343-7_35
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9128
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_981_15_9343_7_35&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Induction%20Motor%20Bearing%20Fault%20Diagnosis%20Using%20Statistical%20Time%20Domain%20Features%20and%20Hypertuning%20of%20Classifiers&offset=0&pcAvailability=true
Publisher
Lecture Notes in Electrical Engineering
Location
독일
Language
영어
Citation Volume
715
Citation Number
1
Citation Start Page
259
Citation End Page
265
Appears in Collections:
Engineering > IT Convergence
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