Industrial process fault diagnosis algorithm based on RLANPE

被引：0

作者：

Mou, Miao ^{[1
]}

Zhao, Xiao-Qiang ^{[1
,2
,3
]}

机构：

[1] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou

[2] Gansu Key Laboratory of Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou

[3] National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou

来源：

Kongzhi yu Juece/Control and Decision | 2025年 / 40卷 / 02期

关键词：

fault diagnosis; industrial process; low-rank representation; manifold learning; neighborhood preserving embedding;

D O I：

10.13195/j.kzyjc.2023.1455

中图分类号：

学科分类号：

摘要：

Fault diagnosis algorithms based on neighborhood preserving embedding (NPE) have been widely used because they can effectively extract the local information of the process. However, the typical NPE method is sensitive to parameter selection and outliers, while ignoring the global information of process data. Therefore, a fault diagnosis algorithm based on robust low-rank adaptive neighborhood preserving embedding (RLANPE) is proposed. This method integrates adaptive neighborhood embedding, projection learning and low-rank representation into a framework, which can effectively extract the local information of data while obtaining global optimal solution. In order to explore the global information of the data and eliminate the influence of outliers, low-rank constraint is imposed on the RLANPE to further enhance the information extraction capability. Meanwhile, the RLANPE introduces projection constraints based on l2,1 norm to select the most discriminative features. The dimension reduction performance and structure preservation capability of the proposed method are verified by three synthetic data sets. The average fault detection rate in Tennessee Eastman can reach 83.72 %, which is nearly 3 % higher than that of the comparison algorithm. © 2025 Northeast University. All rights reserved.

引用

页码：590 / 598

页数：8

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