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ART-Artificial Immune Network and Application in Fault Diagnosis of the Reciprocating Compressor

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4222))

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Abstract

Inspired by complementary strategies, a new fault diagnostic method, which integrates with the Adaptive Resonance Theory (ART) and Artificial Immune Network (AIN), is proposed in this paper. With the help of clustering of ART neural network, the vaccines that image features of data set are extracted effectively, and then an AIN named aiNet is adopted to realize data compression. Finally the memory antibodies optimized by aiNet can be used to recognize each feature of original dataset and to realize fault diagnosis. The experimental results show that the approach is useful and efficient for the fault diagnosis of the multilevel reciprocating compressor.

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Authors and Affiliations

  1. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an, 710049, China

    Maolin Li, Na Wang, Haifeng Du, Jian Zhuang & Sun’an Wang

Authors
  1. Maolin Li
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  2. Na Wang
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  3. Haifeng Du
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  4. Jian Zhuang
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  5. Sun’an Wang
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

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

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., P.O. Box, 710071, Xi’an, P.R. China

    Xinbo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Li, M., Wang, N., Du, H., Zhuang, J., Wang, S. (2006). ART-Artificial Immune Network and Application in Fault Diagnosis of the Reciprocating Compressor. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_62

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  • DOI: https://doi.org/10.1007/11881223_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45907-1

  • Online ISBN: 978-3-540-45909-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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