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Fault Feature Selection Based on Modified Binary PSO with Mutation and Its Application in Chemical Process Fault Diagnosis

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

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Abstract

In large scale industry systems, especially in chemical process industry, large amounts of variables are monitored. When all variables are collected for fault diagnosis, it results in poor fault classification because there are too many irrelevant variables, which also increase the dimensions of data. A novel optimization algorithm, based on a modified binary Particle Swarm Optimization with mutation (MBPSOM) combined with Support Vector Machine (SVM), is proposed to select the fault feature variables for fault diagnosis. The simulations on Tennessee Eastman process (TEP) show the BMPSOM can effectively escape from local optima to find the global optimal value comparing with initial modified binary PSO (MBPSO). And based on fault feature selection, more satisfied performances of fault diagnosis are achieved.

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Authors and Affiliations

  1. Research Institution of Automation, East China University of Science & Technology, 200237, Shanghai, China

    Ling Wang & Jinshou Yu

Authors
  1. Ling Wang
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  2. Jinshou Yu
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Wang, L., Yu, J. (2005). Fault Feature Selection Based on Modified Binary PSO with Mutation and Its Application in Chemical Process Fault Diagnosis. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_102

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  • DOI: https://doi.org/10.1007/11539902_102

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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