LAUSR

Dynamics and nonlinearity may exist in the time and batch directions for batch processes, thereby complicating the monitoring of these processes. In this article, we propose a two-dimensional deep correlated representation learning (2D-DCRL) method to achieve the efficient fault detection and isolation of the nonlinear batch processes. Three-way historical data are first unfolded as two-way time-slice data. Second, a stacked autoencoder based deep neural network is constructed to characterize the correlation among the process variables. Considering that the time and batch directions may be dynamic, for each time-slice measurement, a constructed 2-D measurement containing samples from the previous time instants and batches is then obtained. Subsequently, DCRL is performed between the current running-batch measurements and the constructed 2-D measurements to characterize the 2-D dynamics and nonlinearity. The 2D-DCRL-based monitoring examines the status of a sample by considering the 2-D nonlinear and dynamic information, providing improved monitoring performance. Applications on two typical batch processes demonstrate the effectiveness of the proposed 2D-DCRL monitoring scheme.