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DC Field | Value | Language |
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dc.contributor.author | Ho, SK | - |
dc.contributor.author | Nedunuri, HC | - |
dc.contributor.author | Balachandran, W | - |
dc.contributor.author | Kanfoud, J | - |
dc.contributor.author | Gan, TH | - |
dc.date.accessioned | 2021-12-01T15:05:09Z | - |
dc.date.available | 2021-12-01T15:05:09Z | - |
dc.date.issued | 2021-06-22 | - |
dc.identifier | 5792 | - |
dc.identifier.citation | Ho, S.K., Nedunuri, H.C., Balachandran, W., Kanfoud, J. and Gan, T.-H. (2021) ‘Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach’, Applied Sciences, 11(13), 5792, pp. 1-11 (11). doi: 10.3390/app11135792. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23659 | - |
dc.description.abstract | Copyright: © 2021 by the authors. Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance. | en_US |
dc.description.sponsorship | UK’s innovation agency, Innovate UK under grant agreement number 104505. | en_US |
dc.format.extent | 1 - 11 (11) | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License 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 | blind feature extraction | en_US |
dc.subject | blind source separation (BSS) | en_US |
dc.subject | spectral kurtosis | en_US |
dc.subject | vibration monitoring | en_US |
dc.subject | early fault detection | en_US |
dc.title | Monitoring of industrial machine using a novel blind feature extraction approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/app11135792 | - |
dc.relation.isPartOf | Applied Sciences (Switzerland) | - |
pubs.issue | 13 | - |
pubs.publication-status | Published | - |
pubs.volume | 11 | - |
dc.identifier.eissn | 2076-3417 | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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