Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-Based Input–Output Decoupled Networks

Structural decoupling has played an essential role in model-based fault isolation (FI) and estimation in past decades, which facilitates accurate fault localization and reconstruction thanks to the diagonal transfer matrix design. However, traditional methods exhibit limited effectiveness in modeling high-dimensional nonlinearity and big data, and the decoupling idea has not been well-valued in data-driven frameworks. Known for big data and complex feature extraction capabilities, deep learning has recently been used to develop residual generation models. Nevertheless, it lacks decoupling-related diagnostic designs. To this end, this article proposes a transfer learning (TL)-based input–output decoupled network (TDN) for diagnostic purposes, which consists of an input–output decoupled network (IDN) and a pretrained variational autocoder (VAE). In IDN, uncorrelated residual variables are generated by diagonalization and parallel computing operations. During the TL phase, knowledge of normal status is provided according to VAE’s loss and maximum mean discrepancy loss to guide the training of IDN. After training, IDN learns the mapping from faulty to normal, thereby serving as the fault detection (FD) index and the estimated fault signal simultaneously. At last, the effectiveness of the developed TDN is verified by a numerical example, a chemical simulation, and a real-world chemical production dataset.