Structural decoupling has played an essential role in model-based fault isolation 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 paper proposes a transfer learning-based input-output decoupled network (TDN) for diagnostic purposes, which consists of an input-output decoupled network (IDN) and a pre-trained variational autocoder (VAE). In IDN, uncorrelated residual variables are generated by diagonalization and parallel computing operations. During the transfer learning 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 index and the estimated fault signal simultaneously. At last, the effectiveness of the developed TDN is verified by a numerical example and a chemical simulation.

本文提出了一种基于迁移学习的输入输出解耦网络（TDN）用于诊断目的，它由一个输入输出解耦网络（IDN）和一个预先训练的变分自动编码器（VAE）组成，在IDN中，通过对角化和并行计算操作生成无相关残差变量，在迁移学习阶段，根据VAE的损失和最大均值偏差损失提供正常状态的知识来指导IDN的训练，经过训练后，IDN学习从故障到正常的映射，从而同时作为故障检测指标和估计故障信号。通过数值示例和化学仿真验证了所开发的TDN的有效性。

基于迁移学习的输入输出解耦网络用于化工过程故障诊断中的生成无相关残差变量