Understanding the importance of process alarms based on the analysis of deep recurrent neural networks trained for fault isolation
The identification of process faults is a complex and challenging task due to the high amount of alarms and warnings of control systems. To extract information about the relationships between these discrete events, we utilise multi-temporal sequences of alarm and warning signals as inputs of a recurrent neural network (RNN) based classifier and visualise the network by principal component analysis. The similarity of the events and their applicability in fault isolation can be evaluated based on the linear embedding layer of the network, which maps the input signals into a continuous-valued vector space. The method is demonstrated in a simulated vinyl acetate production technology. The results illustrate that with the application of RNN based sequence learning not only accurate fault classification solutions can be developed, but the visualisation of the model can give useful hints for hazard analysis.
The paper will be published in Journal of Chemometrics, 2018