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首页> 外文期刊>Journal of The institution of engineers (India), Series C >Experimental Investigations on Crack Detection Using Modal Analysis and Prediction of Properties for Multiple Cracks by Neural Network
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Experimental Investigations on Crack Detection Using Modal Analysis and Prediction of Properties for Multiple Cracks by Neural Network

机译：基于模态分析的裂纹检测实验研究及神经网络对多个裂纹性能的预测

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摘要

In the present study, a method is proposed for detection and prediction of properties of multiple transverse cracks on simply supported stepped rotor shaft. Two cases of cracks are considered. Initially, both cracks are perpendicular to axis. Later, both cracks are inclined to vertical plane and also inclined with each other. Modal analysis is performed to extract natural frequency and mode shapes. Finite element method (FEM) is treated as basis for numerical analysis. For validation, experimentation is performed using fast Fourier transform analyzer. Based on natural frequency, cracks are detected. The results of FEM and experimentation are found in agreement. Crack properties are predicted in forward technique using artificial neural networks (ANN). The database of natural frequencies is used to train the network of ANN to predict the crack properties. Applicability of the method is verified by comparing the predictions of ANN with FEM and experimentation. The predictions of ANN and results given by FEM and experimentation are found in agreement. It envisages that the method is competent, suitable and would be alternate to the existing methods. It enhances the performance of structural integrity assessment and online conditioning and monitoring.
机译：在本研究中，提出了一种用于检测和预测简单支撑阶梯式转子轴上多个横向裂纹的特性的方法。考虑了两种情况的裂缝。最初，两个裂纹都垂直于轴。之后，两个裂纹都向垂直平面倾斜并且也彼此倾斜。执行模态分析以提取固有频率和模态形状。有限元法（FEM）被用作数值分析的基础。为了验证，使用快速傅立叶变换分析仪进行实验。根据固有频率，检测裂纹。有限元分析结果与实验结果吻合。裂纹特性是使用人工神经网络（ANN）在正向技术中预测的。固有频率数据库用于训练ANN网络以预测裂纹性质。通过将人工神经网络的预测与有限元进行比较，并通过实验，验证了该方法的适用性。一致发现了人工神经网络的预测以及有限元和实验结果。设想该方法是有效的，合适的并且将替代现有方法。它增强了结构完整性评估以及在线调节和监视的性能。

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