统计过程诊断

基于核Fisher判别分析方法的非线性统计过程监控与故障诊断

Nonlinear statistical process monitoring and fault diagnosis based on kernel Fisher discriminant analysis

提出了基于核主元分析和Fisher判别分析相结合的非线性统计过程监控和故障诊断新方法。

A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis ( KPCA ) and Fisher discriminant analysis ( FDA ) is presented .

人工智能在工序质量控制中的研究与应用有两个重要的方面：基于统计过程控制的过程诊断分析；质量预测控制。

The process diagnosis and analysis based on statistical process control ( SPC ), and predictive control of quality are two major intelligent process quality control ( PQC ) technologies .

指出了基于多元统计过程控制的故障诊断方法具有不需要精确建模、受工业变量间强耦合关系影响小等优点，并概述了其现状及发展趋势。

It is pointed out that fault diagnosis based on multivariate statistical process control has the advantage of no needing of accurately modeling and little effect from the strong correlations among the industrial variables .