您当前所在位置: 首页 > 学者

陆宁云

  • 8浏览

  • 0点赞

  • 0收藏

  • 0分享

  • 490下载

  • 0评论

  • 引用

期刊论文

Combination Method of Principal Component and Wavelet Analysis for Multivariate Process Monitoring and Fault Diagnosis

陆宁云Ningyun Lu Fuli Wang Furong Gao

Ind. Eng. Chem. Res., Vol. 42, No. 18, 2003, 4198-4207,-0001,():

URL:

摘要/描述

Product quality and operation safety are important aspects of industrial processes, particularly those with large numbers of correlated process variables. Principal component analysis(PCA) has been widely used in multivariate process monitoring for its ability to reduce process dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults with similar time-domain process characteristics. A wavelet-based time-frequency approach is developed in this paper to improve PCA-based methods by extending the time-domain process features into time-frequency information. Subsequently, a similarity measure is presented to compare process features for on-line process monitoring and fault diagnosis. Simulation results show that the proposed multivariate time-frequency process feature is effective in both fault detection and diagnosis, illustrating the potentials for real-world application.

关键词:

版权说明:以下全部内容由陆宁云上传于   2008年04月30日 14时08分16秒,版权归本人所有。

我要评论

全部评论 0

本学者其他成果

    同领域成果