Show simple item record

dc.contributor.authorKamiel, Berli Paripurna
dc.contributor.supervisorAssoc. Prof Ian Howard
dc.contributor.supervisorDr Kristoffer McKee
dc.date.accessioned2017-01-30T10:08:43Z
dc.date.available2017-01-30T10:08:43Z
dc.date.created2015-06-10T03:04:11Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/20.500.11937/1532
dc.description.abstract

The thesis proposes a new method for vibration fault diagnosis of centrifugal pumps by combining statistical features, Symlet Wavelet transform, Principal Component Analysis and k-Nearest Neighbors. Six statistical features were extracted from the low frequency part of wavelet decomposition which was then used as input features for the PCA model. The fault detection utilised T2 and Q statistics while fault classification and identification were carried out using score matrices and k-Nearest Neighbors respectively.

dc.languageen
dc.publisherCurtin University
dc.titleVibration-based multi-fault diagnosis for centrifugal pumps
dc.typeThesis
dcterms.educationLevelPh.D.
curtin.departmentDepartment of Mechanical Engineering
curtin.accessStatusOpen access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record