A Deep Non-Stationary Feature Extraction and Feature Fusion Framework for Real Industrial Process Monitoring

With the increasing demands for process safety and manufacturing efficiency, process monitoring has garnered significant attention from both academia and industry over the past few decades. Process monitoring aims to detect deviations from normal operating conditions by analyzing data features extracted under predefined normal states. However, the inherent non-stationarity of real industrial processes can compromise the accurate definition of these normal conditions, thereby limiting the effectiveness of traditional multivariate statistical process monitoring (MSPM) methods. A common strategy to address non-stationarity is to employ projection matrices that transform non-stationary time series into stationary ones, upon which monitoring statistics are constructed. Nevertheless, this approach often overlooks the valuable information contained in the non-stationary subspace, leading to insufficient extraction of fault-relevant features. Fault signatures may manifest in both stationary and non-stationary components of the process data. To overcome these limitations, an integrated monitoring framework that combines Stationary Subspace Analysis (SSA), a Stacked Autoencoder (SAE), and Support Vector Data Description (SVDD) is proposed in this research. Specifically, SSA was first applied to decompose the process data into stationary and non-stationary subspaces. Monitoring statistics were then constructed directly in the stationary subspace, while reconstruction errors from the SAE were used to capture features in the non-stationary subspace. Finally, SVDD was used to fuse the dual-space statistical indicators, enabling comprehensive fault detection. The proposed method was validated by the Tennessee Eastman and real industrial processes. Comparative results demonstrate that it outperformed existing non-stationary monitoring techniques in terms of monitoring performance.