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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 | 2022-04-20T13:09:57Z | - |
dc.date.available | 2022-04-20T13:09:57Z | - |
dc.date.issued | 2022-04-05 | - |
dc.identifier | 511 | - |
dc.identifier.citation | Ahmed, H.O.A. and Nandi, A.K. (2022) ‘Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals’, Entropy, 24 (4), 511, pp. 1 - 28. doi: 10.3390/e24040511. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/24457 | - |
dc.description | Acknowledgements: Authors wish to thank Brunel University London for their support. Data Availability Statement: The data presented in the first case study may be available on request from the first author, Hosameldin O. A. Ahmed. | en_US |
dc.description.abstract | Copyright: © 2022 by the authors. As failures of rolling bearings lead to major failures in rotating machines, recent vibration-based rolling bearing fault diagnosis techniques are focused on obtaining useful fault features from the huge collection of raw data. However, too many features reduce the classification accuracy and increase the computation time. This paper proposes an effective feature selection technique based on intrinsic dimension estimation of compressively sampled vibration signals. First, compressive sampling (CS) is used to get compressed measurements from the collected raw vibration signals. Then, a global dimension estimator, the geodesic minimal spanning tree (GMST), is employed to compute the minimal number of features needed to represent efficiently the compressively sampled signals. Finally, a feature selection process, combining the stochastic proximity embedding (SPE) and the neighbourhood component analysis (NCA), is used to select fewer features for bearing fault diagnosis. With regression analysis-based predictive modelling technique and the multinomial logistic regression (MLR) classifier, the selected features are assessed in two case studies of rolling bearings vibration signals under different working loads. The experimental results demonstrate that the proposed method can successfully select fewer features, with which the MLR-based trained model achieves high classification accuracy and significantly reduced computation times compared to published research. | en_US |
dc.description.sponsorship | This research received no external funding. | en_US |
dc.format.extent | 1 - 28 | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | vibration-based condition monitoring | en_US |
dc.subject | rolling bearing fault diagnosis | en_US |
dc.subject | compressive sampling (CS) | en_US |
dc.subject | feature selection | en_US |
dc.subject | multinomial logistic regression (MLR) | en_US |
dc.title | Intrinsic dimension estimation-based feature selection and multinomial logistic regression for classification of bearing faults using compressively sampled vibration signals | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/e24040511 | - |
dc.relation.isPartOf | Entropy: international and interdisciplinary journal of entropy and information studies | - |
pubs.issue | 4 | - |
pubs.publication-status | Published | - |
pubs.volume | 24 | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers Dept of Electronic and Electrical Engineering Research Papers |
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