Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16745
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dc.contributor.authorAhmed, HOA-
dc.contributor.authorNandi, AK-
dc.date.accessioned2018-08-24T13:42:32Z-
dc.date.available2018-08-24T13:42:32Z-
dc.date.issued2018-09-24-
dc.identifier.citationAhmed, H.O.A. and Nandi, A. K. (2019) 'Three-Stage Hybrid Fault Diagnosis for Rolling Bearings With Compressively Sampled Data and Subspace Learning Techniques,' IEEE Transactions on Industrial Electronics, 66(7), pp. 5516-5524. doi: 10.1109/TIE.2018.2868259.en_US
dc.identifier.issn0278-0046-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/16745-
dc.description.abstractTo avoid the burden of much storage requirements and processing time, this paper proposes a three-stage hybrid method, Compressive Sampling with Correlated Principal and Discriminant Components (CSCPDC), for bearing faults diagnosis based on compressed measurements. In the first stage, Compressive Sampling (CS) is utilised to obtain compressively-sampled signals from raw vibration data. In the second stage, an effective multi-step feature learning algorithm obtains fewer features from correlated principal and discriminant attributes from the compressively-sampled signals, which are then concatenated to increase the performance. In the third stage, with these concatenated features, Multi-class Support Vector Machine (SVM) is used to train, validate, and classify bearing faults. Results show that the proposed method, CS-CPDC, offers high classification accuracies, reduced computation time, and storage requirement, with fewer measurements.en_US
dc.description.sponsorshipNational Science Foundation of China; National Science Foundation of Shanghai;-
dc.format.extent5516 - 5524-
dc.format.mediumPrint-Electronic-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 3.0 License. For more information, see https://creativecommons.org/licenses/by/3.0/-
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/-
dc.subjectmachine condition monitoring (MCM)en_US
dc.subjectbearing fault classificationen_US
dc.subjectcompressive sampling (CS)en_US
dc.subjectprincipal component analysis (PCA)en_US
dc.subjectlinear discriminant analysis (LDA)en_US
dc.subjectcanonical correlation analysis (CCA)en_US
dc.titleThree-stage Hybrid Fault Diagnosis for Rolling Bearings with Compressively-sampled data and Subspace Learning Techniquesen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TIE.2018.2868259-
dc.relation.isPartOfIEEE Transactions on Industrial Electronics-
pubs.issue7-
pubs.publication-statusPublished-
pubs.volume66-
dc.identifier.eissn1557-9948-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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