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DC Field | Value | Language |
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dc.contributor.author | Ahmed, HOA | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2018-08-24T13:42:32Z | - |
dc.date.available | 2018-08-24T13:42:32Z | - |
dc.date.issued | 2018-09-24 | - |
dc.identifier.citation | Ahmed, 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.issn | 0278-0046 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/16745 | - |
dc.description.abstract | To 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.sponsorship | National Science Foundation of China; National Science Foundation of Shanghai; | - |
dc.format.extent | 5516 - 5524 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see https://creativecommons.org/licenses/by/3.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | - |
dc.subject | machine condition monitoring (MCM) | en_US |
dc.subject | bearing fault classification | en_US |
dc.subject | compressive sampling (CS) | en_US |
dc.subject | principal component analysis (PCA) | en_US |
dc.subject | linear discriminant analysis (LDA) | en_US |
dc.subject | canonical correlation analysis (CCA) | en_US |
dc.title | Three-stage Hybrid Fault Diagnosis for Rolling Bearings with Compressively-sampled data and Subspace Learning Techniques | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TIE.2018.2868259 | - |
dc.relation.isPartOf | IEEE Transactions on Industrial Electronics | - |
pubs.issue | 7 | - |
pubs.publication-status | Published | - |
pubs.volume | 66 | - |
dc.identifier.eissn | 1557-9948 | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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