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Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system

Author

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  • He, Yongyong
  • Guo, Dan
  • Chu, Fulei

Abstract

Shaft crack is a very dangerous and frequent fault in rotating machine, but how to locate and configure it is just an inverse problem and not easy to tackle. In this paper, a genetic algorithms based method for shaft crack detection is proposed and described, which formulates the shaft crack detection as an optimization problem by means of finite element method and utilizes genetic algorithms to search the solution. Using genetic algorithms avoids some of the weaknesses of traditional gradient based analytical search methods including the difficulty in constructing well-defined mathematical models directly from practical inverse problems. The numerical experiments suggest that good predictions of the shaft crack locations and configuration are possible and the proposed method is feasible. The study also indicates that the proposed method has the potential to solve a wide range of inverse identification problems in a systematic and robust way.

Suggested Citation

  • He, Yongyong & Guo, Dan & Chu, Fulei, 2001. "Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 57(1), pages 95-108.
    • Handle: RePEc:eee:matcom:v:57:y:2001:i:1:p:95-108
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    Cited by:

    1. Ramasamy, Valarmathi & Kannan, Ramkumar & Muralidharan, Guruprasath & Sidharthan, Rakesh Kumar & Amirtharajan, Rengarajan, 2022. "Two-tier search space optimisation technique for tuning of explicit plant-model mismatch in model predictive controller for industrial cement kiln process," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 385-408.

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