2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

多尺度PCA在传感器故障诊断中的应用研究

徐涛 王祁

徐涛, 王祁. 多尺度PCA在传感器故障诊断中的应用研究. 自动化学报, 2006, 32(3): 417-421.
引用本文: 徐涛, 王祁. 多尺度PCA在传感器故障诊断中的应用研究. 自动化学报, 2006, 32(3): 417-421.
XU Tao, WANG Qi. Application of MSPCA to Sensor Fault Diagnosis. ACTA AUTOMATICA SINICA, 2006, 32(3): 417-421.
Citation: XU Tao, WANG Qi. Application of MSPCA to Sensor Fault Diagnosis. ACTA AUTOMATICA SINICA, 2006, 32(3): 417-421.

多尺度PCA在传感器故障诊断中的应用研究

详细信息
    通讯作者:

    徐涛

Application of MSPCA to Sensor Fault Diagnosis

More Information
    Corresponding author: XU Tao
  • 摘要: A multiscale principal component analysis method is proposed for sensor fault detection and identification. After decomposition of sensor signal by wavelet transform, the coarse-scale coef-ficients from the sensors with strong correlation are employed to establish the principal component analysis model. A moving window is designed to monitor data from each sensor using the model.For the purpose of sensor fault detection and identification, the data in the window is decomposed with wavelet transform to acquire the coarse-scale coefficients firstly, and the square prediction error is used to detect the failure. Then the sensor validity index is introduced to identify faulty sensor,which provides a quantitative identifying index rather than qualitative contrast given by the approach with contribution. Finally, the applicability and effectiveness of the proposed method is illustrated by sensors of industrial boiler.
  • 加载中
计量
  • 文章访问数:  3914
  • HTML全文浏览量:  129
  • PDF下载量:  1505
  • 被引次数: 0
出版历程
  • 收稿日期:  2005-01-14
  • 修回日期:  2005-12-19
  • 刊出日期:  2006-05-20

目录

    /

    返回文章
    返回