摘要：Mixture of probabilistic principal component analyzers (MPPCA) has been used for modeling non-Gaussian process data and monitoring in the past. However, appropriate model structure selection in the case of MPPCA is a challenging task. Previously, variational Bayesian expectation maximization (VBEM) estimation has been used to handle this task. However, VBEM can be computationally expensive for practical purposes and also, may converge to spurious estimates. In this article, collapsed variational Bayesian technique with a new collapsing scheme as an alternative to VBEM is proposed. Advantages of the proposed scheme are demonstrated in simulated and industrial process data.
关键词：Non-Gaussian process monitoringFault detectionModel selectionBayesian estimationCollapsed inference
