Quality-Related Fault Detection Based on Reversible Instance Normalization Temporal Auto-Encoder Canonical Correlation Analysis

In contemporary industrial processes, factors such as raw material fluctuations and noise interference lead to significant changes in the statistical characteristics of data over time, resulting in nonstationary behavior. This nonstationary tends to obscure fault-related information in industrial systems, posing severe challenges for quality-related fault detection. This article proposes a method named reversible instance normalization temporal autoencoder (TAE) canonical correlation analysis (CCA) for quality related fault detection. First, this method quantifies the interdependencies among variables to select process variables that are highly correlated with quality indicators. Subsequently, a reversible normalization module dynamically adjusts normalization parameters to achieve local stationarization of process data. Furthermore, a TAE is utilized to extract temporal features from processed data. Then, a CCA model is established by integrating quality indicators, enabling efficient monitoring of quality indicator under nonstationary conditions. Finally, the proposed method was tested and validated in two real industrial cases.