Study on condition monitoring of power-shift steering transmission based on support vector machine

Ying Feng Zhang; Biao Ma; Yuan Zhu; Jin Le Zhang

doi:10.1109/CISE.2009.5364068

Study on condition monitoring of power-shift steering transmission based on support vector machine

Ying Feng Zhang^*, Biao Ma, Yuan Zhu, Jin Le Zhang

^*此作品的通讯作者

机械与车辆学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

This paper is aimed at the condition monitoring problem of the Power-shift Steering Transmission (PSST), a method of multiple out least squares support vector regression is developed which is applied to prediction of spectrometric oil analysis data. Radial Basis Function (RBF) is used is this algorithm. There are two parameters γ and σ². The selection of γ and σ² is studied using cross validation method with spectrometric oil analysis data. The prediction of spectrometric oil analysis data for PSST is done. A comparative analysis is made between predictive and actual values. The method has been proved that it has better accuracy in prediction, and any possible problem in PSST can be found through a comparative analysis which has important significance for preventing faults.

源语言	英语
主期刊名	Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009
DOI	https://doi.org/10.1109/CISE.2009.5364068
出版状态	已出版 - 2009
活动	2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009 - Wuhan, 中国期限: 11 12月 2009 → 13 12月 2009

出版系列

姓名	Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

会议

会议	2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009
国家/地区	中国
市	Wuhan
时期	11/12/09 → 13/12/09

访问文件

10.1109/CISE.2009.5364068

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, Y. F., Ma, B., Zhu, Y., & Zhang, J. L. (2009). Study on condition monitoring of power-shift steering transmission based on support vector machine. 在 Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009 文章 5364068 (Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009). https://doi.org/10.1109/CISE.2009.5364068

Zhang, Ying Feng ; Ma, Biao ; Zhu, Yuan 等. / Study on condition monitoring of power-shift steering transmission based on support vector machine. Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009. 2009. (Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009).

@inproceedings{483c26e6d51f4436877f4d1d954fdbd1,

title = "Study on condition monitoring of power-shift steering transmission based on support vector machine",

abstract = "This paper is aimed at the condition monitoring problem of the Power-shift Steering Transmission (PSST), a method of multiple out least squares support vector regression is developed which is applied to prediction of spectrometric oil analysis data. Radial Basis Function (RBF) is used is this algorithm. There are two parameters γ and σ2. The selection of γ and σ2 is studied using cross validation method with spectrometric oil analysis data. The prediction of spectrometric oil analysis data for PSST is done. A comparative analysis is made between predictive and actual values. The method has been proved that it has better accuracy in prediction, and any possible problem in PSST can be found through a comparative analysis which has important significance for preventing faults.",

keywords = "Condition monitoring, Power-Shift Steering Transmission (PSST), Support Vector Machine (SVM)",

author = "Zhang, {Ying Feng} and Biao Ma and Yuan Zhu and Zhang, {Jin Le}",

year = "2009",

doi = "10.1109/CISE.2009.5364068",

language = "English",

isbn = "9781424445073",

series = "Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009",

booktitle = "Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009",

note = "2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009 ; Conference date: 11-12-2009 Through 13-12-2009",

}

Zhang, YF, Ma, B, Zhu, Y & Zhang, JL 2009, Study on condition monitoring of power-shift steering transmission based on support vector machine. 在 Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009., 5364068, Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009, 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009, Wuhan, 中国, 11/12/09. https://doi.org/10.1109/CISE.2009.5364068

Study on condition monitoring of power-shift steering transmission based on support vector machine. / Zhang, Ying Feng; Ma, Biao; Zhu, Yuan 等.
Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009. 2009. 5364068 (Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Study on condition monitoring of power-shift steering transmission based on support vector machine

AU - Zhang, Ying Feng

AU - Ma, Biao

AU - Zhu, Yuan

AU - Zhang, Jin Le

PY - 2009

Y1 - 2009

N2 - This paper is aimed at the condition monitoring problem of the Power-shift Steering Transmission (PSST), a method of multiple out least squares support vector regression is developed which is applied to prediction of spectrometric oil analysis data. Radial Basis Function (RBF) is used is this algorithm. There are two parameters γ and σ2. The selection of γ and σ2 is studied using cross validation method with spectrometric oil analysis data. The prediction of spectrometric oil analysis data for PSST is done. A comparative analysis is made between predictive and actual values. The method has been proved that it has better accuracy in prediction, and any possible problem in PSST can be found through a comparative analysis which has important significance for preventing faults.

AB - This paper is aimed at the condition monitoring problem of the Power-shift Steering Transmission (PSST), a method of multiple out least squares support vector regression is developed which is applied to prediction of spectrometric oil analysis data. Radial Basis Function (RBF) is used is this algorithm. There are two parameters γ and σ2. The selection of γ and σ2 is studied using cross validation method with spectrometric oil analysis data. The prediction of spectrometric oil analysis data for PSST is done. A comparative analysis is made between predictive and actual values. The method has been proved that it has better accuracy in prediction, and any possible problem in PSST can be found through a comparative analysis which has important significance for preventing faults.

KW - Condition monitoring

KW - Power-Shift Steering Transmission (PSST)

KW - Support Vector Machine (SVM)

UR - http://www.scopus.com/inward/record.url?scp=77949673933&partnerID=8YFLogxK

U2 - 10.1109/CISE.2009.5364068

DO - 10.1109/CISE.2009.5364068

M3 - Conference contribution

AN - SCOPUS:77949673933

SN - 9781424445073

T3 - Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

BT - Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

T2 - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

Y2 - 11 December 2009 through 13 December 2009

ER -

Zhang YF, Ma B, Zhu Y, Zhang JL. Study on condition monitoring of power-shift steering transmission based on support vector machine. 在 Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009. 2009. 5364068. (Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009). doi: 10.1109/CISE.2009.5364068

Study on condition monitoring of power-shift steering transmission based on support vector machine

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