A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes

► A kernel Gaussian mixture model based inferential monitoring method is proposed. ► Geometric distance based fault detection in nonlinear kernel feature space. ► Inferential contribution decomposition for fault diagnosis. ► Superior capability in handling multi-Gaussianity and within-mode nonlinearity.