Distributed Gaussian mixture model for monitoring plant-wide processes with multiple operating modes

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For large-scale plant-wide processes with multiple operating conditions, a distributed Gaussian mixture modeling and monitoring mechanism is proposed. To overcome the deficient prior modeling knowledge for complex process, a two-dimensional probabilistic topic model based technique named Bayesian co-clustering method is developed to simultaneously conduct the sub-block division and operating mode recognition. With the obtained sub-blocks and operating modes, a global Gaussian mixture model is first built and then several local Gaussian mixture models are extracted and applied for distributed monitoring of plant-wide processes. By conducting distributed modeling and monitoring, both global and local process changes can be reflected and the fault region can be localized more easily for further analyses. The feasibility and effectiveness of the proposed method is confirmed through a numerical example and the Tennessee Eastman benchmark process.