Higher-order correlation–based multivariate statistical process monitoring

As shallow architecture is inefficient in terms of computational elements, some incipient fault features can be characterized through the composition of many nonlinearities, ie, with deep network. In this paper, a novel approach is developed for multivariate statistical process monitoring based on higher-order correlations. First, the correlations among monitoring variables can be learned by a multilayer learning framework hierarchically: The higher the number of layers to be stacked, the more nonlinear and abstract features can be characterized. Second, 3 monitoring statistics, SRE, M², and C, are presented to monitor whether the process is remaining in control, and they are instructive for the identification of fault types. Moreover, only normal data are used in training phase; this can avoid the unbalance problem of different types of fault data. These capabilities of the proposed approach are illustrated with two industrial benchmarks, Tennessee Eastman process and Metal Etch process.