| Summary: | Determining the variable transmission structure is the key step in designing a distributed monitoring scheme for multiunit processes. This paper proposes randomized algorithm (RA) integrated with evolutionary optimization-based data-driven distributed local fault detection scheme to achieve efficient monitoring of multiunit chemical processes. First, the RA is employed to generate faulty validation data. Second, evolutionary optimization-based variable transmission structure determination is performed to achieve the minimal non-detection rate by selecting transferred variables. Then, a principal component analysis (PCA) or kernel PCA monitoring model is established for each operation unit to identify the status of the unit. Last, a comprehensive index is developed to identify the status of the entire process. The established local monitors consider the relationship with other units but avoid introducing redundant information, thereby exhibiting superior monitoring performance. Case studies on two numerical examples, including a linear and a nonlinear case and the Tennessee Eastman benchmark process, are provided. Comparative results of traditional local or global monitoring methods verify the efficiency of the proposed monitoring scheme.
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