Health condition estimation of bearings with multiple faults by a composite learning-based approach

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dc.contributor.authorInyang, Udeme
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorJennions, Ian
dc.date.accessioned2021-07-01T14:17:12Z
dc.date.available2021-07-01T14:17:12Z
dc.date.issued2021-06-28
dc.description.abstractBearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.en_UK
dc.identifier.citationInyang U, Petrunin I, Jennions I. (2021) Health condition estimation of bearings with multiple faults by a composite learning-based approach. Sensors, Volume 21, Issue 13, June 2021, Article number 4424en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s21134424
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16837
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmultiple faultsen_UK
dc.subjectdiagnosticsen_UK
dc.subjectcomplementaryen_UK
dc.subjectdeep learningen_UK
dc.subjecthealth managementen_UK
dc.titleHealth condition estimation of bearings with multiple faults by a composite learning-based approachen_UK
dc.typeArticleen_UK

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