Identification of crack location and depth in rotating machinery based on artificial neural network

  • Authors:

  • Tao Yu;Qing-Kai Han;Zhao-Ye Qin;Bang-Chun Wen

  • Affiliations:

  • School of Mechanical Engineering & Automation, Northeastern University, Shenyang, P.R. China;School of Mechanical Engineering & Automation, Northeastern University, Shenyang, P.R. China;School of Mechanical Engineering & Automation, Northeastern University, Shenyang, P.R. China;School of Mechanical Engineering & Automation, Northeastern University, Shenyang, P.R. China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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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.