A nonlinear method for monitoring industrial process

Abstract

Aiming at fault detection in industrial processes with nonlinear or high dimensions, a novel method based on locally linear embedding preserve neighborhood for fault detection is proposed in this paper. Locally linear embedding preserve neighborhood is a feature-mapping method that combines Locally linear embedding and Laplacian eigenmaps algorithms. First, two weight matrices are obtained by the Locally linear embedding and Laplacian eigenmaps, respectively. Subsequently, the two weight matrices are combined by a balance factor to obtain the objective function. Locally linear embedding preserve neighborhood method can effectively maintain the characteristics of data in high-dimensional space. The purpose of dimension reduction is to map the high-dimensional data to low-dimensional space by optimizing the objective function. Process monitoring is performed by constructing T2 and Q statistics. To demonstrate its effectiveness and superiority, the proposed locally linear embedding preserve neighborhood for fault detection method is tested under the Swiss Roll dataset and an industrial case study. Compared with traditional fault detection methods, the proposed method in this paper effectively improves the detection rate and reduces the false alarm rate.

Fluid catalytic cracking process quality-driven fault detection based on partial least squares and deep feedforward neural network

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Multimode process monitoring strategy based on improved just‐in‐time‐learning associated with locality preserving projections

“…Manifold learning methods, which focus on internal structure information of the data, are proposed. [18,19] Manifold learning aims to represent a set of data in lowdimensional space so that the data can reflect certain essential structural characteristics of the raw highdimensional data. As the typical representation, locality preserving projections (LPP) have been widely used in the field of process monitoring.…”

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

“…Recently, process data have been studied and applied for monitoring (Ge, 2017;Li et al, 2020a), control (Jose et al, 2019), optimization (Xie et al, 2021), prediction (Zhang et al, 2019c(Zhang et al, , 2020b, and so on. In the field of process monitoring, multivariate statistical analysis methods (MSAM), such as principal component analysis (PCA) and partial least squares (PLS) which do not require precise modeling of the process, have played an important role (Nawaz et al, 2021;Zhang et al, 2020c).…”