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Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

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Abstract
Condition monitoring is used to track the unavoidable phases of rolling element bearings
in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The
convolutional neural network (CNN) has been used as an effective tool to recognize and classify
multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of
vibration signals, it is quite difficult to achieve high classification accuracy when directly using the
original signal as the input of a convolution neural network. To evaluate the fault characteristics,
ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into
multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant
IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the
reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration
signal is converted into a 2-D image using a continuous wavelet transform with information from the
damage frequency band. This also transfers the signal into a time-frequency domain and reduces the
nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions,
which possess a discriminative pattern relative to the types of faults, are used to train an appropriate
CNN model. Additionally, with the reconstructed signal, two different methods are used to create
an image to compare with our proposed image creation approach. The vibration signal is collected
from a self-designed testbed containing multiple bearings of different fault conditions. Two other
conventional CNN architectures are compared with our proposed model. Based on the results
obtained, it can be concluded that the image generated with fault signatures not only accurately
classifies multiple faults with CNN but can also be considered as a reliable and stable method for the
diagnosis of fault bearings.
Author(s)
토마 라피아 니샤트김철홍김종면
Issued Date
2021
Type
Article
Keyword
bearing fault diagnosisCWTEEMDenvelope analysistime-frequency representationvibration signal
DOI
10.3390/electronics10111248
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9148
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_fed98971d5604d7eaefb249f974bae15&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Bearing%20Fault%20Classification%20Using%20Ensemble%20Empirical%20Mode%20Decomposition%20and%20Convolutional%20Neural%20Network&offset=0&pcAvailability=true
Publisher
ELECTRONICS
Location
스위스
Language
영어
ISSN
2079-9292
Citation Volume
10
Citation Number
11
Citation Start Page
1248
Citation End Page
1248
Appears in Collections:
Engineering > IT Convergence
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