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Feature Extraction Method for Fault Diagnosis of Machine Unit Based on Wavelet Singularity Principle and Immunology Optimization Principle

  • Jian CenEmail author
  • Yinbo Wu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 133)

Abstract

Consider on fault signal coupling of machine unit for fault feature extraction caused by difficult problems, wavelet singularity theory be used complex fault feature extraction. Fault signal after wavelet denosing, which use clonal selection for fault classification. In the new feature space, the characteristics of different types of fault modes enhanced data aggregation, the different fault can be divided, and the compound fault signal will be separated, thereby enhancing the accuracy of fault diagnosis, fault samples of analog complex machine unit be trained, detected, and diagnosed, results to be verified.

Keywords

wavelet singularity principle clonal selection fault diagnosis 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of AutomationGuangdong Polytechnic Normal UniversityGuangzhouChina

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