中文摘要：

针对多工况过程建立了一个多工况高斯混合模型（Gaussian Mixture Model,GMM）,并利用EM（Expectation Maximization）算法对该GMM参数进行估计。通过引入贝叶斯阴阳算法（Bayesian YingYang,BYY）,实现了GMM中混合工况数目的自动估计。然后,通过在所建GMM的每个分量中构建PCA模型,建立一个多工况故障监控混合模型。最后利用TE过程研究证明了所建模型在过程监控中的有效性。

英文摘要：

A muhimode Gaussian Mixture Model （GMM） was established and estimated with Expectation Maximization （EM） algorithm, including the mixture mode number associated with the EM algorithm through introducing Bayesian Ying-Yang （BYY） algorithm. By constructing Principal Component Analysis （PCA） mo- nitoring model in GMM＇s each component, a multimode fault monitoring mixture model was established. The Tennessee Eastman （TE） benchmark proves effectiveness of the proposed model in process monitoring.