  A Deep Neural Network (DNN) based algorithm is proposed for the detection and
classification of faults in industrial plants. The proposed algorithm has the
ability to classify faults, especially incipient faults that are difficult to
detect and diagnose with traditional threshold based statistical methods or by
conventional Artificial Neural Networks (ANNs). The algorithm is based on a
Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that
uses dynamic information of the process along the time horizon. Based on this
network a hierarchical structure is formulated by grouping faults based on
their similarity into subsets of faults for detection and diagnosis. Further,
an external pseudo-random binary signal (PRBS) is designed and injected into
the system to identify incipient faults. The hierarchical structure based
strategy improves the detection and classification accuracy significantly for
both incipient and non-incipient faults. The proposed approach is tested on the
benchmark Tennessee Eastman Process resulting in significant improvements in
classification as compared to both multivariate linear model-based strategies
and non-hierarchical nonlinear model-based strategies.
