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Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

机译:基于经验模态分解的非线性系统辨识方法在节理梁螺栓松动检测中的应用

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In this work, a state-of-art nonlinear system identification method based on empirical mode decomposition is utilized and extended to detect bolt loosening in a jointed beam. This nonlinear system identification method is based on identifying the multi-scale dynamics of the underlying system. Only structural dynamic response signals are needed to construct a reduced-order model to represent the system concerned. It makes the method easy to use in practice. A new bolt loosening identification procedure based on the constructed system nonlinear reduced-order model is proposed. A new damage feature to indicate bolt loosening is presented. Experimental works are carried out to validate the proposed method. The results show that the proposed damage detection method can detect bolt loosening effectively, and the proposed damage feature values increase with the increase of bolt torques. The damage feature calculated from the response solution of the reduced-order model can give robust and sensitive indication of bolt loosening.
机译:在这项工作中,利用了基于经验模态分解的最新非线性系统识别方法,并将其扩展为检测节理梁中的螺栓松动。这种非线性系统识别方法基于识别基础系统的多尺度动力学。仅需要结构动态响应信号即可构建降阶模型来表示所涉及的系统。它使该方法易于在实践中使用。提出了一种基于系统非线性降阶模型的螺栓松动识别新方法。提出了一种新的损坏功能以指示螺栓松动。实验工作进行了验证该方法。结果表明,所提出的损伤检测方法能够有效地检测螺栓的松动,并且所提出的损伤特征值随螺栓扭矩的增加而增加。根据降阶模型的响应解计算得出的损坏特征可以为螺栓松动提供可靠而敏感的指示。

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