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A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

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Abstract
Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal
pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this
paper proposes a novel automated health state diagnosis framework for centrifugal pump that combines
a signal to time-frequency imaging technique and an Adaptive Deep Convolution Neural Network model
(ADCNN). First, the vibration signals corresponding to different health conditions of the centrifugal pump
are acquired. Vibration signals obtained from the centrifugal pump carry a great deal of information and
generally, statistical features are extracted from the vibration signals to retain meaningful fault information.
However, these features are either insensitive to weak incipient faults or unsuitable for tracking severe faults,
thus, decreasing the fault classification accuracy. To tackle this problem, a signal to time-frequency imaging
technique is applied to the pump vibration signals. For this purpose, Continuous Wavelet Transform (CWT)
is applied to decompose the vibration signals over different time-frequency scales and extract the pump fault
information in both the time and frequency domains. The CWT scales form two-dimensional time-frequency
images commonly referred to as scalograms. The CWT scalograms are then converted into grayscale images
(SGI). Over the past few decades, CNN models have been established as an effective practice to process
images for classification and pattern recognition. Consequently, the extracted CWTSGIs are finally provided
as inputs to the proposed ADCNN architecture to achieve feature extraction and classification for centrifugal
pump faults. The performance of the proposed diagnostic framework (CWTSGI + ADCNN) is validated
with a vibration dataset collected from a testbed specifically designed for centrifugal pump diagnosis. The
experimental results suggest that the proposed technique based on CWTSGI and ADCNN outperformed
existing methods with an average performance improvement of 4.7 ? 15.6%.
Author(s)
주나예드 엠디Akhand Rai아흐마드 자후르김종면
Issued Date
2021
Type
Article
Keyword
Centrifugal pumpcontinuous wavelet transformationsContinuous wavelet transformsconvolutional neural networkFault diagnosisFeature extractiongray imagesPumpsscalogramTime-frequency analysisVibrationsWavelet transforms
DOI
10.1109/ACCESS.2021.3072854
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9140
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_58a16d81c43c4a2ebcf518b85d298fe4&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,A%20Fault%20Diagnosis%20Framework%20for%20Centrifugal%20Pumps%20by%20Scalogram-Based%20Imaging%20and%20Deep%20Learning&offset=0&pcAvailability=true
Publisher
IEEE ACCESS
Location
미국
Language
영어
ISSN
2169-3536
Citation Volume
9
Citation Number
1
Citation Start Page
58052
Citation End Page
58066
Appears in Collections:
Engineering > IT Convergence
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