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Multivariate Fault Isolation in Presence of Outliers Based on Robust Nonnegative Garrote

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Intelligent Computing, Networked Control, and Their Engineering Applications (ICSEE 2017, LSMS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 762))

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Abstract

Fault isolation is essential to fault monitoring, which can be used to detect the cause of the fault. Commonly used methods include contribution plots, LASSO, Nonnegative garrote, construction-based methods, branch and bound algorithm (B & B), etc. However, these existing methods have shortcomings limiting their implementation when there exist vertical outliers and leverage points, Therefore, to further improve the fault prediction accuracy, this paper present a strategy based on robust nonnegative garrote (R-NNG) variable selection algorithm, which is proved to be robust to outliers in the TE process.

This work is supported by National Natural Science Foundation (NNSF) of China under Grant 61171145 and 61374044.

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Authors and Affiliations

  1. Shanghai Key Lab of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Jianguo Wang, Zhifu Deng, Banghua Yang, Shiwei Ma & Minrui Fei

  2. Department of Chemical Engineering, National Tsing-Hua University, Hsin-Chu, 30013, Taiwan

    Yuan Yao

  3. Department of Chemical and Process Engineering, University of Surrey, Guildford, GU2 7XH, UK

    Tao Chen

Authors
  1. Jianguo Wang
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  2. Zhifu Deng
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  3. Banghua Yang
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  4. Shiwei Ma
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  5. Minrui Fei
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  6. Yuan Yao
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  7. Tao Chen
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Corresponding author

Correspondence to Zhifu Deng .

Editor information

Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Dong Yue

  2. Shanghai University , Shanghai, China

    Chen Peng

  3. Shanghai University , Shanghai, China

    Dajun Du

  4. Nanjing University of Posts and Telecommunications, Nanjing, China

    Tengfei Zhang

  5. Shanghai University , Shanghai, China

    Min Zheng

  6. Swinburne University of Technology, Melbourne, Victoria, Australia

    Qinglong Han

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Wang, J. et al. (2017). Multivariate Fault Isolation in Presence of Outliers Based on Robust Nonnegative Garrote. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_38

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  • DOI: https://doi.org/10.1007/978-981-10-6373-2_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6372-5

  • Online ISBN: 978-981-10-6373-2

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