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A novel just-in-time kernel modeling method is proposed to online fault detection and diagnosis for chemical processes. The model parameters can be suitably selected using a fast cross-validation strategy. For a query sample, an online kernel classifier is constructed adaptively in a just-in-time manner for mode identification, i.e., fault detection and diagnosis, using the most relevant samples around it. The superiority of the proposed kernel classifier is demonstrated through a simulated chemical example, compared with the related method with fixed parameters. Keywords-fault detection and diagnosis; mode identification; kernel classifier; just-in-time learning

Just-in-time Kernel Classifier for Online Process Diagnosis