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Identification of Crack Location and Depth in Rotating Machinery Based on Artificial Neural Network

  • Tao Yu
  • Qing-Kai Han
  • Zhao-Ye Qin
  • Bang-Chun Wen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

With the characteristics of ANN’s strong capability on nonlinear approximation, a new method by combining an artificial neural network with back-propagation learning algorithm and modal analysis via finite element model of cracked rotor system is proposed for fast identification of crack fault with high accuracy in rotating machinery. First, based on fracture mechanics and the energy principle of Paris, the training data are generated by a set of FE-model-based equations in different crack cases. Then the validation of the method is verified by several selected crack cases. The results show that the trained ANN models have good performance to identify the crack location and depth with higher accuracy and efficiency, further, can be used in fast identification of crack fault in rotating machinery.

Keywords

Mode Shape Artificial Neural Network Model Rotor System Crack Depth Transverse Crack 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tao Yu
    • 1
  • Qing-Kai Han
    • 1
  • Zhao-Ye Qin
    • 1
  • Bang-Chun Wen
    • 1
  1. 1.School of Mechanical Engineering & AutomationNortheastern UniversityShenyangP.R. China

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