Advanced Feature Extraction and Dimensionality Reduction for Unmanned Underwater Vehicle Fault Diagnosis
dc.contributor.author | Abed, W | |
dc.contributor.author | Polvara, R | |
dc.contributor.author | Singh, Y | |
dc.contributor.author | sharma, sanjay | |
dc.contributor.author | Sutton, R | |
dc.contributor.author | Hatton, Daniel | |
dc.contributor.author | Manning, Andrew | |
dc.contributor.author | Wan, Jian | |
dc.date.accessioned | 2016-10-24T14:49:05Z | |
dc.date.accessioned | 2016-10-24T14:56:42Z | |
dc.date.accessioned | 2016-11-03T10:53:40Z | |
dc.date.accessioned | 2016-11-20T09:31:16Z | |
dc.date.accessioned | 2016-11-20T09:38:39Z | |
dc.date.accessioned | 2016-11-20T10:39:22Z | |
dc.date.accessioned | 2016-11-20T11:42:49Z | |
dc.date.accessioned | 2016-11-20T12:09:58Z | |
dc.date.accessioned | 2016-11-20T12:11:16Z | |
dc.date.accessioned | 2016-11-24T14:43:39Z | |
dc.date.available | 2016-10-24T14:49:05Z | |
dc.date.available | 2016-10-24T14:56:42Z | |
dc.date.available | 2016-11-03T10:53:40Z | |
dc.date.issued | 2016-11-11 | |
dc.identifier.isbn | 9781467398916 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/8017 | |
dc.description.abstract |
This paper presents a novel approach to the diagnosis of blade faults in an electric thruster motor of unmanned underwater vehicles (UUVs) under stationary operating conditions. The diagnostic approach is based on the use of discrete wavelet transforms (DWT) as a feature extraction tool and a dynamic neural network (DNN) for fault classification. The DNN classifies between healthy and faulty conditions of the trolling motor by analyzing the stator current and vibration signals. To overcome feature redundancy, which affects diagnosis reliability, the Orthogonal Fuzzy Neighbourhood Discriminant Analysis (OFNDA) approach is found to be the most effective. Four faulty conditions were analyzed under laboratory conditions, and the results obtained from experiment demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and more accurately. | |
dc.format.extent | 1-6 | |
dc.language.iso | en | |
dc.publisher | IEEE | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6611 | |
dc.relation.replaces | 10026.1/6611 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6613 | |
dc.relation.replaces | 10026.1/6613 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6670 | |
dc.relation.replaces | 10026.1/6670 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6767 | |
dc.relation.replaces | 10026.1/6767 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6768 | |
dc.relation.replaces | 10026.1/6768 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6769 | |
dc.relation.replaces | 10026.1/6769 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6770 | |
dc.relation.replaces | 10026.1/6770 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6771 | |
dc.relation.replaces | 10026.1/6771 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/6772 | |
dc.relation.replaces | 10026.1/6772 | |
dc.subject | dimensionality reduction | |
dc.subject | dynamic neural network | |
dc.subject | fault diagnosis | |
dc.subject | feature extraction | |
dc.title | Advanced Feature Extraction and Dimensionality Reduction for Unmanned Underwater Vehicle Fault Diagnosis | |
dc.type | conference | |
dc.type | Conference Proceeding | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000388667900086&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.date-start | 2016-08-31 | |
plymouth.date-finish | 2016-09-02 | |
plymouth.publisher-url | http://ieeexplore.ieee.org/document/7737596/ | |
plymouth.conference-name | 11th UKACC International Conference on Control | |
plymouth.publication-status | Published | |
plymouth.journal | The 2016 UKACC International Conference on Control (UKACC Control 2016) | |
dc.identifier.doi | 10.1109/CONTROL.2016.7737596 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Biological and Marine Sciences | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA07 Earth Systems and Environmental Sciences | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering | |
plymouth.organisational-group | /Plymouth/Research Groups | |
plymouth.organisational-group | /Plymouth/Research Groups/Marine Institute | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
plymouth.organisational-group | /Plymouth/Users by role/Researchers in ResearchFish submission | |
dc.publisher.place | Belfast | |
dc.publisher.place | Danvers | |
dcterms.dateAccepted | 2016-07-31 | |
dc.rights.embargoperiod | No embargo | |
rioxxterms.versionofrecord | 10.1109/CONTROL.2016.7737596 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2016-11-11 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract |