Industrial processes often operate under diverse conditions to fulfill manufacturing strategies. A common approach involves segmenting data based on operational modes and developing tailored learning models for each. However, data from the same mode can still exhibit complex patterns due to non-Gaussian behaviors and fault occurrences. Merely analyzing coarse-grained correlations between modes may insufficiently partition modes. Addressing this, this chapter introduces a novel fault identification method for multi-mode industrial processes. Initially, a hierarchical clustering strategy captures the multi-grained information within process data, modeling correlations both across modes (different operation conditions) and within modes (patterns in each mode). Subsequently, a feature learning algorithm leveraging non-negative matrix factorization (NMF) is introduced to extract data features, enabling samples to be represented by the identified multi-grained structural information. A weighted metric is also designed to accurately measure the feature similarities obtained through the NMF. Notably, our framework employs an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _{p}$$\end{document}-norm \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(0
minimization strategy to deal with noise and outliers in practical processes. The efficacy of the proposed framework is demonstrated through a numerical example and the multi-phase flow process.
Multi-grained Matrix Factorization for Processes Fault Identification Under Multi-mode Working Conditions
eng. Applications of Computational Methods
2025-04-16
22 pages
Article/Chapter (Book)
Electronic Resource
English
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