An apparatus comprising: at least one memory (304, 310, 312) configured to store operating data associated with operation of a cross-directional industrial process that is controlled by at least one model-based process controller (106, 204); and at least one processing device configured to: perform closed-loop model identification during a training period (502a, 502b), using a sliding window to repeatedly identify spatial, temporal and noise model parameters using different parts of data in the training periods; identify clusters associated with model parameters using a support vector machine to determine clustering of the model parameters, the support vector machine implements a kernel function that generates linear or nonlinear clustering of data by mapping original data into a space where a cluster boundary is identified and the support vector machine is used for binary classifications; perform closed-loop model identification with routine operating data in a testing period to identify additional spatial, temporal and noise model parameters; determine whether any of the additional spatial, temporal and noise model parameters fall outside of the clusters identified using the spatial, temporal and noise model parameters from the prior training period; and in response to the determination detect that a mismatch exists between the model parameters identified during the training period and the testing period of the cross-directional industrial process. at least one memory (304, 310, 312) configured to store operating data associated with operation of a cross-directional industrial process that is controlled by at least one model-based process controller (106, 204); and at least one processing device configured to: perform closed-loop model identification during a training period (502a, 502b), using a sliding window to repeatedly identify spatial, temporal and noise model parameters using different parts of data in the training periods; identify clusters associated with model parameters using a support vector machine to determine clustering of the model parameters, the support vector machine implements a kernel function that generates linear or nonlinear clustering of data by mapping original data into a space where a cluster boundary is identified and the support vector machine is used for binary classifications; perform closed-loop model identification with routine operating data in a testing period to identify additional spatial, temporal and noise model parameters; determine whether any of the additional spatial, temporal and noise model parameters fall outside of the clusters identified using the spatial, temporal and noise model parameters from the prior training period; and in response to the determination detect that a mismatch exists between the model parameters identified during the training period and the testing period of the cross-directional industrial process. perform closed-loop model identification during a training period (502a, 502b), using a sliding window to repeatedly identify spatial, temporal and noise model parameters using different parts of data in the training periods; identify clusters associated with model parameters using a support vector machine to determine clustering of the model parameters, the support vector machine implements a kernel function that generates linear or nonlinear clustering of data by mapping original data into a space where a cluster boundary is identified and the support vector machine is used for binary classifications; perform closed-loop model identification with routine operating data in a testing period to identify additional spatial, temporal and noise model parameters; determine whether any of the additional spatial, temporal and noise model parameters fall outside of the clusters identified using the spatial, temporal and noise model parameters from the prior training period; and in response to the determination detect that a mismatch exists between the model parameters identified during the training period and the testing period of the cross-directional industrial process.