Enhancing fault detection and diagnosis systems for a chemical process: a study on convolutional neural networks and transfer learning

The study and development of fault detection and diagnosis (FDD) systems are relevant tasks for industrial processes. Another prominent field is applying deep learning (DL) models to solve engineering problems, such as FDD systems’ design. Often, the preliminary tests are conducted using simulated datasets to verify the chosen methodology and avoid unnecessarily disturbing the real process. Even if the data used come from a computer simulation, it must remain as realistic as possible. In several studies, researchers have used the Tennessee Eastman Process (TEP) benchmark for addressing the application of DL models to build effective FDD frameworks. However, most of them use preexisting datasets, and this presents some drawbacks that can negatively impact the DL model’s training stage. In addition, none of them have evaluated how to adjust the existing FDD model when the process control strategy is changed. This paper presents various topologies of convolutional neural networks (CNNs) to model a FDD system for the TEP benchmark using new datasets. For the first time, we investigate the performance of fully convolutional networks (FCNs) in the TEP study case. Additionally, we apply transfer learning (TL) to surpass the model inadequacy when the data distribution changes due to an alteration in the process’ closed-loop system.