Tool wear monitoring using acoustic emissions by dominant-feature identification

10.1109/TIM.2010.2050974

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Bibliographic Details
Main Authors:	Zhou, J.-H., Pang, C.K., Zhong, Z.-W., Lewis, F.L.
Other Authors:	ELECTRICAL & COMPUTER ENGINEERING
Format:	Article
Published:	2014
Subjects:	Auto-Regressive Moving Average with eXogenous inputs (ARMAX) model least squares error (LSE) principal component analysis (PCA) principal feature analysis (PFA) singular value decomposition (SVD) tool condition monitoring (TCM)
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/57682
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-57682
record_format	dspace
spelling	sg-nus-scholar.10635-576822023-10-30T21:55:44Z Tool wear monitoring using acoustic emissions by dominant-feature identification Zhou, J.-H. Pang, C.K. Zhong, Z.-W. Lewis, F.L. ELECTRICAL & COMPUTER ENGINEERING Auto-Regressive Moving Average with eXogenous inputs (ARMAX) model least squares error (LSE) principal component analysis (PCA) principal feature analysis (PFA) singular value decomposition (SVD) tool condition monitoring (TCM) 10.1109/TIM.2010.2050974 IEEE Transactions on Instrumentation and Measurement 60 2 547-559 IEIMA 2014-06-17T03:08:59Z 2014-06-17T03:08:59Z 2011-02 Article Zhou, J.-H., Pang, C.K., Zhong, Z.-W., Lewis, F.L. (2011-02). Tool wear monitoring using acoustic emissions by dominant-feature identification. IEEE Transactions on Instrumentation and Measurement 60 (2) : 547-559. ScholarBank@NUS Repository. https://doi.org/10.1109/TIM.2010.2050974 00189456 http://scholarbank.nus.edu.sg/handle/10635/57682 000286008700023 Scopus
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Auto-Regressive Moving Average with eXogenous inputs (ARMAX) model least squares error (LSE) principal component analysis (PCA) principal feature analysis (PFA) singular value decomposition (SVD) tool condition monitoring (TCM)
spellingShingle	Auto-Regressive Moving Average with eXogenous inputs (ARMAX) model least squares error (LSE) principal component analysis (PCA) principal feature analysis (PFA) singular value decomposition (SVD) tool condition monitoring (TCM) Zhou, J.-H. Pang, C.K. Zhong, Z.-W. Lewis, F.L. Tool wear monitoring using acoustic emissions by dominant-feature identification
description	10.1109/TIM.2010.2050974
author2	ELECTRICAL & COMPUTER ENGINEERING
author_facet	ELECTRICAL & COMPUTER ENGINEERING Zhou, J.-H. Pang, C.K. Zhong, Z.-W. Lewis, F.L.
format	Article
author	Zhou, J.-H. Pang, C.K. Zhong, Z.-W. Lewis, F.L.
author_sort	Zhou, J.-H.
title	Tool wear monitoring using acoustic emissions by dominant-feature identification
title_short	Tool wear monitoring using acoustic emissions by dominant-feature identification
title_full	Tool wear monitoring using acoustic emissions by dominant-feature identification
title_fullStr	Tool wear monitoring using acoustic emissions by dominant-feature identification
title_full_unstemmed	Tool wear monitoring using acoustic emissions by dominant-feature identification
title_sort	tool wear monitoring using acoustic emissions by dominant-feature identification
publishDate	2014
url	http://scholarbank.nus.edu.sg/handle/10635/57682
_version_	1781781455266906112

Tool wear monitoring using acoustic emissions by dominant-feature identification

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