Two-level multiblock statistical monitoring for plant-wide processes

다국어 초록 (Multilingual Abstract)

Due to the complexity of plant-wide processes, many of the current multivariate statistical process monitoring
techniques are lacking in interpretation of the detected fault, and fault identification also becomes difficult. A new
two-level multiblock independent component analysis and principal component analysis (MBICA-PCA) method is
proposed in this paper. Different from the conventional method, the new approach can incorporate block information
into the high level for global process monitoring. Through the new method, the process monitoring task can be greatly
reduced and the interpretation for the process can be made more quickly. When a fault is detected, a two-step fault
identification method is proposed. The responsible sub-block is first identified by contribution plots, which is followed
by fault reconstruction in the corresponding sub-block for advanced fault identification. A case study of the Tennessee
Eastman (TE) process evaluates the feasibility and efficiency of the proposed method.

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