Dimensionality reducing Gaussian mixture-based reconstruction for fault detection in multimode processes

Modern industrial processes increasingly prioritize demands for safety and reliability, spurring substantial research on process monitoring models. Among existing research subjects, concurrent multimode operating conditions are vital for effective process monitoring. This work proposes an efficient dimensionality-reducing Gaussian mixture-based reconstruction approach for multimode industrial process monitoring. The t-SNE method is first employed to transform high-dimensional data into a lower-dimensional space that retains critical operational information. Using these reduced dimensions, a robust Gaussian mixture model is established to partition the operation data into different modes. Furthermore, the original data are assigned to the corresponding operating modes, and local variational autoencoder (VAE) reconstruction models are established, respectively. For each VAE model, two statistics are designed, termed $T^2$ and $\mathrm{SPE}$, to detect abnormalities. The proposed method is applied to a three-phase flow facility, and the superiority over the comparison methods is proved.