A multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections

Abstract

For multimode process monitoring, accurate mode information is difficult to be obtained, and each mode is monitored separately, which increases the complexity of the system. This paper proposes a multimode process monitoring strategy via improved variational inference Gaussian mixture model based on locality preserving projections (IVIGMM-LPP). First, the raw data are projected to the feature space where samples still maintain the original neighbor structure. Second, a new discriminant condition is introduced to reduce the influence of the initial category parameter on the iteration results in the VIGMM model. Then, the data are updated utilizing modal information, so that the scales of different modes are adjusted to the same level. Next, the deviation vector is introduced to eliminate the multi-center structure of data. Finally, the statistic is built to monitor the process. IVIGMM-LPP establishes one model for monitoring the premise of knowing the mode information, which reduces the complexity of the monitoring process and improves the fault detection rate. The experimental results of a numerical case and the Tennessee Eastman (TE) process verify the effectiveness of IVIGMM-LPP.

A fault diagnosis method based on label-wise density-domain space learning

“…LPP uses Euclidean distance as the metric criterion and Euclidean distance as the key information of the structure in maintaining the original space manifold structure [25]. The Euclidean distance formula is as follows:…”

“…However, both methods focus on global information among samples, but local news needs to be addressed. Therefore, many local space learning-based methods have emerged, such as Laplacian eigenmaps [23], locally linear embedding [24], and locality-preserving projection (LPP) [25]. However, Laplacian eigenmaps and locally linear embedding are not optimistic about the closed and density-uneven sample distribution, limiting their separate ability.…”

Progress of Process Monitoring for the Multi-Mode Process: A Review

“…This method avoided modal misclassification. Guo et al [54] established an improved variational inference Gaussian mixture model based on locality preserving projection (IVIGMM-LPP). The model preprocesses data using locality preserving projection (LPP), introduces the eigenvalue discrimination condition of the covariance matrix and the standard deviation vector to reduce the influence of initial parameters and unify data, and builds an overall GMM model for monitoring.…”

Feature extraction and fault detection scheme via improved locality preserving projection and SVDD

“…Because they can generate a detailed map, which is linear and can easily be obtained like PCA. Several variations have been proposed to classical LPP and NPE (Guo et al, 2022; Yao et al, 2022), such as improved local entropy locality preserving projections (ILELPP; (Guo et al, 2019), which forms the local entropy LPP. The method removes the non-Gaussian characteristics using entropy LPP and showed improved process monitoring.…”