Fault Detection-Based Multiple Local Manifold Learning and Its Application to Blast Furnace Ironmaking Process
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Published:2023-11-24
Issue:23
Volume:12
Page:4773
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Wang Ke1,
Wu Ping1,
Lou Siwei2,
Pan Haipeng1ORCID,
Gao Jinfeng1
Affiliation:
1. School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
2. College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Abstract
Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning methods including Laplacian eigenmaps, locally linear embedding, and Hessian eigenmaps have been utilized in data-driven fault detection. However, only the partial local structure information is extracted from the aforementioned methods. This paper proposes fused local manifold learning (FLML), which synthesizes the typical manifold learning methods to find the underlying manifold structure from different angles. A more comprehensive local structure is discovered under a unified framework by constructing an objection optimization function for process data dimension reduction. The proposed method takes advantage of different manifold learning methods. Based on the proposed dimension reduction method, a new data-driven fault detection method is developed. Hotelling’s T2 and Q statistics are established for the purpose of fault detection. Experiments on an industrial benchmark Tennessee Eastman process whose average MDR and average FAR of FLML T2 are 7.58% and 0.21% and a real blast furnace ironmaking process whose MDR and FAR of FLML T2 are 2.80% and 0.00% are carried out to demonstrate the superiority and effectiveness of the proposed method.
Funder
Zhejiang Province Public Welfare Technology Application Research Project
Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering