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Fault Diagnosis for an Automatic Shell Magazine Using FDA and ELM

  • Qiangqiang Zhao
  • Lingfeng Tao
  • Maosheng Li
  • Peng Hong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

A fault diagnosis method for an automatic shell magazine based on Functional Data Analysis (FDA) and Extreme Learning Machine (ELM) is presented in this paper. A virtual prototype model of the automatic shell magazine includes a mechanical model and control model is built in RecurDyn and Simulink. The failure mechanism of the automatic shell magazine is analyzed, and the corresponding fault factors are selected. Due to an insufficient number of fault samples, a large number of fault samples are generated by the virtual prototype model and the fault samples are analyzed by FDA. Then, the eigenvalues from FDA are used to train ELM to obtain a diagnostic machine. The diagnostic machine is used for the fault diagnosis of the automatic shell magazine and is proved to be very effective.

Keywords

Fault diagnosis Automatic shell magazine Functional data analysis Extreme learning machine 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Qiangqiang Zhao
    • 1
  • Lingfeng Tao
    • 1
  • Maosheng Li
    • 1
  • Peng Hong
    • 1
  1. 1.Jiangsu Jinling Institute of Intelligent Manufacturing Co. Ltd.NanjingPeople’s Republic of China

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