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contribution charts
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  贡献图
     The contribution charts are used to undertake fault diagnosis. The simulation results show that PCA is an effective approach to fault detection and can only work for the simple sensor fault diagnosis. A novel idea to combine PCA with causal model based approach is presented for the future research aiming at complex sensor fault and internal process fault.
     讨论了基于主元分析 ( PCA)的过程故障检测与诊断的原理 ,运用 T2统计、Q统计方法 ,结合贡献图对一典型过程进行了仿真分析 ,结果表明 PCA方法可对简单传感器故障进行检测与诊断 ,并指出了该方法中的不足 ,提出了将 PCA方法同基于过程动态模型的故障诊断方法相结合的研究思路
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  相似匹配句对
     The Contribution of Qingbao
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     On Technology Contribution
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     Synthetic Control Charts
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  contribution charts
Also, contribution charts are used to identify variables that are single point violations in order to facilitate the investigation of an excursion.
      


Fault detection and diagnosis method based on the principal components analysis (PCA) is discussed . The fault detection and diagnosis simulation to a typical chemical process is performed by means of statistical methods like Holleting T 2 and Q. The contribution charts are used to undertake fault diagnosis. The simulation results show that PCA is an effective approach to fault detection and can only work for the simple sensor fault diagnosis. A novel idea to combine PCA with causal model based approach...

Fault detection and diagnosis method based on the principal components analysis (PCA) is discussed . The fault detection and diagnosis simulation to a typical chemical process is performed by means of statistical methods like Holleting T 2 and Q. The contribution charts are used to undertake fault diagnosis. The simulation results show that PCA is an effective approach to fault detection and can only work for the simple sensor fault diagnosis. A novel idea to combine PCA with causal model based approach is presented for the future research aiming at complex sensor fault and internal process fault.

讨论了基于主元分析 ( PCA)的过程故障检测与诊断的原理 ,运用 T2统计、Q统计方法 ,结合贡献图对一典型过程进行了仿真分析 ,结果表明 PCA方法可对简单传感器故障进行检测与诊断 ,并指出了该方法中的不足 ,提出了将 PCA方法同基于过程动态模型的故障诊断方法相结合的研究思路

A method of fault detection and diagnosis based on wavelet de-noise and principal component analysis is proposed.The data collected from the normal industry condition are processed by means of the wavelet analysis.The fault detection and diagnosis simulation to a model is performed by means of statistical method like Hotelling T~2 and Q.The principal component scores charts and variables contribution charts are used to undertake fault diagnosis.Simulation results show that it is fairly effective.

提出了一种基于小波去噪和主元分析的故障检测和诊断方法。该方法利用小波分析先对正常工况下的数据进行处理,然后运用T2统计、Q统计方法,结合主元得分图和变量贡献图对一模型进行了仿真分析,结果表明,该方法是有效的。

>=A method of fault detection and diagnosis based on wavelet denoising and principal component analysis is proposed in this paper. The data collected from the normal industry condition are processed by means of the wavelet analysis. The fault detection and diagnosis simulation to a model is performed by means of statistical method like Hotelling T2 and Q. The principal component scores charts and variables contribution charts are used to undertake fault diagnosis. Simulation results show that it is fairly...

>=A method of fault detection and diagnosis based on wavelet denoising and principal component analysis is proposed in this paper. The data collected from the normal industry condition are processed by means of the wavelet analysis. The fault detection and diagnosis simulation to a model is performed by means of statistical method like Hotelling T2 and Q. The principal component scores charts and variables contribution charts are used to undertake fault diagnosis. Simulation results show that it is fairly effective.

提出了一种基于小波去噪和主元分析的故障检测和诊断方法。该方法利用小波分析先对正常工况下的数据进行处理,然后运用T2统计、Q统计方法,结合主元得分图和变量贡献图对一模型进行了仿真分析。结果表明,该方法是有效的。

 
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