Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings
Nonlinear process monitoring using a mixture of probabilistic PCA with clusterings
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使用概率 PCA 与聚类相结合的非线性过程监控
DOI：
10.1016/j.neucom.2021.06.039
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发表时间：
2021-10
期刊：
影响因子：
6
通讯作者：
Xia Hong
中科院分区：
文献类型：
--
作者：
Jingxin Zhang;Maoyin Chen;Xia Hong
Motivated by mixture of probabilistic principal component analysis (PCA), which is time-consuming due to expectation maximization, this paper investigates a novel mixture of probabilistic PCA with clusterings for process monitoring. The significant features are extracted by singular vector decomposition (SVD) or kernel PCA, andk-means is subsequently utilized as a clustering algorithm. Then, parameters of local PCA models are determined under each clustering model. Compared with PCA clustering, SVD based clustering only utilizes the nature basis for the components of the data instead of principal components of the data. Three clustering approaches are adopted and the effectiveness of the proposed approach is demonstrated by a practical coal pulverizing system.
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