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- 2018
基于应变能和隶属度的结构损伤识别研究
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Abstract:
为了解决测量噪声等引起的损伤识别结果不确定问题，提出了一种基于模态应变能和隶属度的损伤识别方法.首先描述了云模型的基本理论及其数字特征；然后给出了基于模态应变能的计算公式及其损伤指标，即模态应变能耗散率指标和模态应变能等效指标，并将两种指标作为云模型的相关参数；最后对逆向云发生器和云模型数字特征估计值进行了分析，并基于隶属度函数等量优化原则提出了一种应变能隶属云损伤识别方法，并利用了Udwadia方法来模拟产生随机试验测量数据，采用了多次测量产生的不确定数据进行了损伤识别研究.数值算例结果表明，提出的应变能隶属云方法可以较好地处理测量噪声引起的不确定问题，其对含噪数据的识别结果明显优于单纯的模态应变能耗散率指标和模态应变能等效指标.
The results of damage detection are often uncertain due to measurement noise. In order to solve this problem, a damage detection method based on modal strain energy and membership function is presented in this paper. First, the basic theory and numerical characteristics of cloud model are described. Then, the modal strain energy dissipation ratio index (MSEDRI) and the modal strain energy equivalence index (MSEEI) are analyzed, and the two indexes are considered as identification dynamic parameters. Finally, a backward cloud generator and the numerical characteristic estimation of the cloud model are analyzed, and a strain energy membership cloud (SEMC) method based on modal strain energy and membership function is proposed. Random test data are generated using the Udwadia method, and multiple measurement uncertain data are used to identify structural damage. The simulation results indicate that the proposed SEMC method can solve the uncertain damage problem, and the identification results of the SEMC method are obviously better than those of both modal strain energy dissipation ratio index and modal strain energy equivalence index
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