Interpretable Self-Attention Dynamic Inner Neural Network Model for Nonlinear Dynamic Process Monitoring

Nonlinearity and dynamics are important issues in industrial process monitoring. The dynamic relationships between samples are often embedded in the latent variable space and are usually nonlinear. The fitting of nonlinear dynamic relationships largely determines the monitoring performance. In addition to the effectiveness of fitting, low computational complexity and high interpretability are also crucial. To address these issues, this study proposes a novel process monitoring algorithm called the dynamic inner neural network based on the simplified self-attention mechanism (DiNN-SSAM). This algorithm maps the process variables into the latent variable space that best reflects the dynamic relationships between samples through neural networks. Then, this algorithm fits the dynamic relationships between samples through the simplified self-attention mechanism. DiNN-SSAM not only establishes an accurate dynamic model but also reduces the complexity of online computation. In addition, this study creatively provides the fault detectability analysis of neural-network-based process monitoring algorithms and further fault sensitivity analysis, which compensates for the weak interpretability of neural networks. Finally, the superior performance of DiNN-SSAM is verified by the simulations of numerical examples, the Tennessee Eastman (TE) process, and catalytic cracking units.