Black History Month is here! Discover ERA research focused on Black experiences in Canada and worldwide. Use our general search below to get started!

Principal components of super-high dimensional statistical features and support vector machine for improving identification accuracies of different gear crack levels under different working conditions

Simple item page
dc.contributor.authorTsui, Kwok Leung
dc.contributor.authorZuo, Ming J.
dc.contributor.authorTse, Peter W.
dc.contributor.authorWang, Dong
dc.date.accessioned2025-05-01T12:23:17Z
dc.date.available2025-05-01T12:23:17Z
dc.date.issued2015
dc.descriptionGears are widely used in gearbox to transmit power from one shaft to another. Gear crack is one of the most frequent gear fault modes found in industry. Identification of different gear crack levels is beneficial in preventing any unexpected machine breakdown and reducing economic loss because gear crack leads to gear tooth breakage. In this paper, an intelligent fault diagnosis method for identification of different gear crack levels under different working conditions is proposed. First, superhigh-dimensional statistical features are extracted from continuous wavelet transform at different scales. The number of the statistical features extracted by using the proposed method is 920 so that the extracted statistical features are superhigh dimensional. To reduce the dimensionality of the extracted statistical features and generate new significant low-dimensional statistical features, a simple and effective method called principal component analysis is used. To further improve identification accuracies of different gear crack levels under different working conditions, support vector machine is employed. Three experiments are investigated to show the superiority of the proposed method. Comparisons with other existing gear crack level identification methods are conducted. The results show that the proposed method has the highest identification accuracies among all existing methods.
dc.identifier.doihttps://doi.org/10.7939/R32V2CQ0T
dc.language.isoen
dc.relationhttp://dx.doi.org/10.1155/2015/420168
dc.relation.isversionofWang, D., Tsui, K., Tse, P., and Zuo, M. (). Principal components of super-high dimensional statistical features and support vector machine for improving identification accuracies of different gear crack levels under different working conditions. Shock and Vibration, 2015(420168 ), .
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectVibration Signal Analysis
dc.subjectFault-Diagnosis
dc.subjectDamage
dc.subjectScalogram
dc.subjectDecomposition
dc.titlePrincipal components of super-high dimensional statistical features and support vector machine for improving identification accuracies of different gear crack levels under different working conditions
dc.typehttp://purl.org/coar/resource_type/c_6501 http://purl.org/coar/version/c_970fb48d4fbd8a85
ual.jupiterAccesshttp://terms.library.ualberta.ca/public

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Shock-20and-20Vibration_2015_2015_4201768.pdf
Size:
3.11 MB
Format:
Adobe Portable Document Format
Download

Collections

Journal Articles (Mechanical Engineering)