ART-Artificial immune network and application in fault diagnosis of the reciprocating compressor

  • Authors:

  • Maolin Li;Na Wang;Haifeng Du;Jian Zhuang;Sun'an Wang

  • Affiliations:

  • Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China

  • Venue:
  • ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
  • Year:
  • 2006

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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.