非线性状态监测数据下的磨损定位与状态识别

Shu Fa Yan; Biao Ma; Chang Song Zheng; Jian Wen Chen; Hui Zhu Li

doi:10.13229/j.cnki.jdxbgxb20171072

非线性状态监测数据下的磨损定位与状态识别

Translated title of the contribution: Wear localization and identification under nonlinear condition monitoring data

Shu Fa Yan, Biao Ma, Chang Song Zheng^*, Jian Wen Chen, Hui Zhu Li

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

A new wear localization and state identification method of mechanical transmission system is proposed based on improved Principal Component Analysis (PCA) and cluster analysis, which considers the data nonlinearity and outliers. The nonlinear and robust improvement of the PCA is realized by nonlinear mapping and fuzzy set theory. Moreover, a nonlinear data discrimination method is proposed based on principal component clustering. Finally, a case study for power shift steering transmission is conducted. The results demonstrate that the proposed method is superior to the method without considering the nonlinear data and outliers, and it can effectively improve the accuracy of wear localization and state identification.

Translated title of the contribution	Wear localization and identification under nonlinear condition monitoring data
Original language	Chinese (Traditional)
Pages (from-to)	359-365
Number of pages	7
Journal	Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
Volume	49
Issue number	2
DOIs	https://doi.org/10.13229/j.cnki.jdxbgxb20171072
Publication status	Published - 1 Mar 2019

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title = "非线性状态监测数据下的磨损定位与状态识别",

abstract = "A new wear localization and state identification method of mechanical transmission system is proposed based on improved Principal Component Analysis (PCA) and cluster analysis, which considers the data nonlinearity and outliers. The nonlinear and robust improvement of the PCA is realized by nonlinear mapping and fuzzy set theory. Moreover, a nonlinear data discrimination method is proposed based on principal component clustering. Finally, a case study for power shift steering transmission is conducted. The results demonstrate that the proposed method is superior to the method without considering the nonlinear data and outliers, and it can effectively improve the accuracy of wear localization and state identification.",

keywords = "Cluster analysis, Mechanical transmission system, Principal components analysis, State identification, Vehicle engineering, Wear location",

author = "Yan, {Shu Fa} and Biao Ma and Zheng, {Chang Song} and Chen, {Jian Wen} and Li, {Hui Zhu}",

year = "2019",

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T1 - 非线性状态监测数据下的磨损定位与状态识别

AU - Yan, Shu Fa

AU - Ma, Biao

AU - Zheng, Chang Song

AU - Chen, Jian Wen

AU - Li, Hui Zhu

PY - 2019/3/1

Y1 - 2019/3/1

N2 - A new wear localization and state identification method of mechanical transmission system is proposed based on improved Principal Component Analysis (PCA) and cluster analysis, which considers the data nonlinearity and outliers. The nonlinear and robust improvement of the PCA is realized by nonlinear mapping and fuzzy set theory. Moreover, a nonlinear data discrimination method is proposed based on principal component clustering. Finally, a case study for power shift steering transmission is conducted. The results demonstrate that the proposed method is superior to the method without considering the nonlinear data and outliers, and it can effectively improve the accuracy of wear localization and state identification.

AB - A new wear localization and state identification method of mechanical transmission system is proposed based on improved Principal Component Analysis (PCA) and cluster analysis, which considers the data nonlinearity and outliers. The nonlinear and robust improvement of the PCA is realized by nonlinear mapping and fuzzy set theory. Moreover, a nonlinear data discrimination method is proposed based on principal component clustering. Finally, a case study for power shift steering transmission is conducted. The results demonstrate that the proposed method is superior to the method without considering the nonlinear data and outliers, and it can effectively improve the accuracy of wear localization and state identification.

KW - Cluster analysis

KW - Mechanical transmission system

KW - Principal components analysis

KW - State identification

KW - Vehicle engineering

KW - Wear location

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DO - 10.13229/j.cnki.jdxbgxb20171072

M3 - 文章

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SN - 1671-5497

VL - 49

SP - 359

EP - 365

JO - Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)

JF - Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)

IS - 2

ER -

非线性状态监测数据下的磨损定位与状态识别

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