Nonparametric manifold learning approach for improved process monitoring

Abstract

A novel nonparametric method based on manifold learning is proposed for industrial process monitoring. In conventional algorithms, to preserve the global and local structure information of data, heat kernels containing two auxiliary parameters are introduced to define the global and local weight matrices, respectively. However, it is difficult to identify and choose these two parameters empirically. The inadequate selection of parameters can lead to one‐sided and inappropriate global and local feature extractions, resulting in an inadequate fault detection performance. To resolve the above problems, a nonparametric strategy is used in this study to generate two nonparametric weight matrices to replace the heat kernel‐based weight matrices. Consequently, the proposed method requires no auxiliary parameters in defining the weight matrices, making it more practical. Moreover, it automatically determines a good trade‐off between global and local feature extractions. A process monitoring model based on the proposed method was developed. The feasibility and effectiveness of the new nonparametric method are evaluated using a synthetic example and the Tennessee Eastman chemical process.

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

“…The major difference among these methods is located in a way; they integrate the objective function of PCA and LPP. Similarly, Chen et al (2019), Cui et al (2021), Miao et al (2015), and Tan et al (2019) combine PCA with NPE. These new manifold learning methods showed significant improvement in fault detection performance.…”

Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

“…With different forms of local feature extraction, NPE and LTSA were later extended to extract global and local features of the data for fault detection [17][18][19]. Due to its lesser limitations on data distribution and low computational complexity, various GLPP-based improvements have been proposed, including dynamic, nonlinearity, non-parameterization, sparsity, and ensemble learning [20][21][22][23][24][25][26][27].…”

An explicit nonlinear mapping‐based locality constrained index for nonlinear statistical process monitoring

“…Particularly, dimensionality reduction techniques, like principal component analysis (PCA) and manifold learning approaches, have been widely investigated in the circle of MSPM. [4][5][6] The essential point of MSPM methods focuses on deriving a normal region from the dataset given the normal operating condition (NOC), and the deviation from the derived region then indicates the occurrence of an abnormal sample. With respect to the growing complexity of industrial plants, the nonlinearity in the sampled data added new challenges to process monitoring.…”