A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions
dc.contributor.author | Abed, W | |
dc.contributor.author | sharma, sanjay | |
dc.contributor.author | Sutton, R | |
dc.contributor.author | Motwani, A | |
dc.date.accessioned | 2015-10-15T10:58:28Z | |
dc.date.available | 2015-10-15T10:58:28Z | |
dc.date.issued | 2015-06 | |
dc.identifier.issn | 2195-3880 | |
dc.identifier.issn | 2195-3899 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/3638 | |
dc.description.abstract |
Rolling element bearing defects are among the main reasons for the breakdown of electrical machines, and therefore, early diagnosis of these is necessary to avoid more catastrophic failure consequences. This paper presents a novel approach for identifying rolling element bearing defects in brushless DC motors under non-stationary operating conditions. Stator current and lateral vibration measurements are selected as fault indicators to extract meaningful features, using a discrete wavelet transform. These features are further reduced via the application of orthogonal fuzzy neighbourhood discriminative analysis. A recurrent neural network is then used to detect and classify the presence of bearing faults. The proposed system is implemented and tested in simulation on data collected from an experimental setup, to verify its effectiveness and reliability in accurately detecting and classifying the various faults. | |
dc.format.extent | 241-254 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Springer Science and Business Media LLC | |
dc.title | A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.issue | 3 | |
plymouth.volume | 26 | |
plymouth.publication-status | Published | |
plymouth.journal | Journal of Control, Automation and Electrical Systems | |
dc.identifier.doi | 10.1007/s40313-015-0173-7 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
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/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 | |
dc.identifier.eissn | 2195-3899 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1007/s40313-015-0173-7 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review |