Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines

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Príomhchruthaitheoirí:	Islam, Md Rashedul, Uddin, Jia, Kim, Jongmyon
Rannpháirtithe:	Department of Computer Science and Engineering, BRAC University
Formáid:	Alt
Teanga:	English
Foilsithe / Cruthaithe:	© 2016 Old City Publishing, Inc. 2018
Ábhair:	Acoustic emission Discrete wavelet transform Fault diagnosis Gabor filter Induction motor Wireless sensor network
Rochtain ar líne:	http://hdl.handle.net/10361/9576

id	10361-9576
record_format	dspace
spelling	10361-95762018-03-05T10:35:25Z Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines Islam, Md Rashedul Uddin, Jia Kim, Jongmyon Department of Computer Science and Engineering, BRAC University Acoustic emission Discrete wavelet transform Fault diagnosis Gabor filter Induction motor Wireless sensor network This article was published in the Ad-Hoc and Sensor Wireless Networks [© 2016 Old City Publishing, Inc.] Reliable and efficient fault diagnosis of induction motors is an important issue in industrial environments. This paper proposes a method for reliable fault diagnosis of induction motors using signal processing of acoustic emission (AE) data, including Gabor filtering and the use of multiclass support vector machines (MCSVMs), where a ZigBee based wireless sensor network (WSN) model is used for efficiently transmitting AE signals to a diagnosis server. In the proposed fault diagnosis approach, the induction motor’s different state signals are acquired through proper placement of AE sensors. The AE data are sent to a server through the wireless sensor network and decomposed using discrete wavelet transformation (DWT). An appropriate band is then selected using the maximum energy ratio, and a one-dimensional (1D) Gabor filter with various frequencies and orientation angles is applied to reduce abnormalities and extract various statistical parameters for generating features. In addition, principal component analysis (PCA) is applied to the extracted features to select the most dominant feature dimensions. Finally, one-against-one multiclass support vector machines (OAA-MCSVMs) are used to classify multiple fault types of an induction motor, where each SVM individually trains with its own features to increase the fault classification accuracy of the induction motor. In experiments, the proposed approach achieved an average classification accuracy of 99.80%, outperforming conventional fault diagnosis models. Published 2018-03-05T10:33:58Z 2018-03-05T10:33:58Z 2016 Article Islam, M. R., Uddin, J., & Kim, J. -. (2016). Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines. Ad-Hoc and Sensor Wireless Networks, 34(1-4), 273-287. 15519899 http://hdl.handle.net/10361/9576 en © 2016 Old City Publishing, Inc.
institution	Brac University
collection	Institutional Repository
language	English
topic	Acoustic emission Discrete wavelet transform Fault diagnosis Gabor filter Induction motor Wireless sensor network
spellingShingle	Acoustic emission Discrete wavelet transform Fault diagnosis Gabor filter Induction motor Wireless sensor network Islam, Md Rashedul Uddin, Jia Kim, Jongmyon Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
description	This article was published in the Ad-Hoc and Sensor Wireless Networks [© 2016 Old City Publishing, Inc.]
author2	Department of Computer Science and Engineering, BRAC University
author_facet	Department of Computer Science and Engineering, BRAC University Islam, Md Rashedul Uddin, Jia Kim, Jongmyon
format	Article
author	Islam, Md Rashedul Uddin, Jia Kim, Jongmyon
author_sort	Islam, Md Rashedul
title	Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
title_short	Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
title_full	Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
title_fullStr	Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
title_full_unstemmed	Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
title_sort	acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines
publisher	© 2016 Old City Publishing, Inc.
publishDate	2018
url	http://hdl.handle.net/10361/9576
work_keys_str_mv	AT islammdrashedul acousticemissionsensornetworkbasedfaultdiagnosisofinductionmotorsusingagaborfilterandmulticlasssupportvectormachines AT uddinjia acousticemissionsensornetworkbasedfaultdiagnosisofinductionmotorsusingagaborfilterandmulticlasssupportvectormachines AT kimjongmyon acousticemissionsensornetworkbasedfaultdiagnosisofinductionmotorsusingagaborfilterandmulticlasssupportvectormachines
_version_	1814307335115374592

Acoustic emission sensor network based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines

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