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Arabian Journal for Science and Engineering

, Volume 43, Issue 11, pp 5859–5869 | Cite as

Joint Global and Local Discriminant Embedding for Multi-fault Process Monitoring and Fault Classification

  • Chunhong LuEmail author
  • Jiehua Wang
Research Article - Chemical Engineering
  • 58 Downloads

Abstract

This paper proposes a new manifold learning-based scheme for multi-fault detection and classification, which utilizes local and nonlocal embedding method to build a statistic index for fault detection and subsequently develops a joint global and local discriminant embedding (GLDE) approach to discover the discriminant features of multiple faults for fault classification. The proposed GLDE approach can capture the global and local/nonlocal structure information of complicated data and obtain the concise discriminant information for classification. Compared with the conventional Fisher discriminant analysis method, GLDE has a strong discriminant power and provides better monitoring results for complex multi-fault Tennessee Eastman process.

Keywords

Fault detection and classification Manifold learning Global and local discriminant embedding Multi-fault process Tennessee Eastman process 

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Notes

Compliance with ethical standards

Conflicts of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyNantong UniversityNantongChina

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