  In processing and manufacturing industries, there has been a large push to
produce higher quality products and ensure maximum efficiency of processes.
This requires approaches to effectively detect and resolve disturbances to
ensure optimal operations. While the control system can compensate for many
types of disturbances, there are changes to the process which it still cannot
handle adequately. It is therefore important to further develop monitoring
systems to effectively detect and identify those faults such that they can be
quickly resolved by operators. In this paper, a novel probabilistic fault
detection and identification method is proposed which adopts a newly developed
deep learning approach using Bayesian recurrent neural networks~(BRNNs) with
variational dropout. The BRNN model is general and can model complex nonlinear
dynamics. Moreover, compared to traditional statistic-based data-driven fault
detection and identification methods, the proposed BRNN-based method yields
uncertainty estimates which allow for simultaneous fault detection of chemical
processes, direct fault identification, and fault propagation analysis. The
outstanding performance of this method is demonstrated and contrasted to
(dynamic) principal component analysis, which are widely applied in the
industry, in the benchmark Tennessee Eastman process~(TEP) and a real chemical
manufacturing dataset.
