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Permanent link (DOI): https://doi.org/10.7939/R32V2CQ0T

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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 Open Access

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Author or creator
Wang, Dong
Tsui, Kwok Leung
Tse, Peter W.
Zuo, Ming J.
Additional contributors
Subject/Keyword
Decomposition
Damage
Vibration Signal Analysis
Fault-Diagnosis
Scalogram
Type of item
Journal Article (Published)
Language
English
Place
Time
Description
Gears 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.
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doi:10.7939/R32V2CQ0T
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Attribution 4.0 International
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Wang, 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 ), .
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