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Fault Diagnosis of Inclined Edge Cracked Cantilever Beam Using Vibrational Analysis and Artificial Intelligence Techniques

机译:基于振动分析和人工智能技术的倾斜边缘裂纹悬臂梁故障诊断。

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摘要

Damage is one of the vital characteristics in structural analysis because of safety cause as well as economic prosperity of the industries. The existence of cracks which influence the performance of structures as well as the vibrational parameters like modal natural frequencies, mode shapes, modal damping and stiffness. In this research paper, the effect of crack parameters (relative crack location & crack depth, and crack inclination) on the vibrational parameters of a single inclined edge crack cantilever beam are examined by different techniques using numerical method, finite element analysis (FEA), AI techniques (FUZZY inference method and Artificial Neural Network). Experimental analysis is carried out for verifying the results.Finite Element Method has been accomplished to derive the vibration signatures of the inclined cracked cantilever beam. The results obtained analytically are validated with the results obtained from the FEA. The simulations of FEA have done with the help of ANSYS software. Different artificial intelligent techniques based on Fuzzy controller and Artificial Neural Network controller have been formulated using the computed vibrational parameters for inclined edge crack identification in cantilever beam elements with more precision and significantly low computational period.
机译:由于安全原因以及行业的经济繁荣,损坏是结构分析中的重要特征之一。裂纹的存在会影响结构的性能以及振动参数,如模态固有频率,模态形状,模态阻尼和刚度。本文采用数值方法,有限元分析(FEA),不同方法研究了裂纹参数(相对裂纹位置和裂纹深度,裂纹倾角)对单斜边裂纹悬臂梁振动参数的影响。 AI技术(模糊推理方法和人工神经网络)。进行了实验分析,验证了结果。有限元方法用于导出倾斜裂纹悬臂梁的振动特征。从FEA获得的结果验证了分析获得的结果。 FEA的仿真是借助ANSYS软件完成的。利用计算出的振动参数,提出了基于模糊控制器和人工神经网络控制器的各种人工智能技术,用于悬臂梁单元斜边裂纹的识别,具有较高的精度和较低的计算周期。

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    Kumar Ranjan;

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  • 年度 2014
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