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Classification of eddy current signals using fuzzy logic and neural networks

机译:基于模糊逻辑和神经网络的涡流信号分类

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Abstract: The nondestructive eddy current methods are commonly used for automated defect inspection to detect cracks in materials which are used in cars, power and aircraft industries. The eddy current signal from a infinitely long crack can be classified with the help of the fuzzy logic and the neural network techniques. A rule based fuzzy logic classification guarantees better results than fuzzy-cluster- means algorithm, because the classification results can be increased in this case step by step. By using the neural network for the classification of the crack signals it is very important to have a good 'learning pattern.' The advantage of time-delay networks in this application is the fact that the network can 'learn' the eddy-current time signal; a signal preprocessing is not necessary. !9
机译:摘要:无损涡流方法通常用于自动缺陷检查,以检测汽车,电力和飞机行业中使用的材料中的裂纹。来自无限长裂纹的涡流信号可以借助模糊逻辑和神经网络技术进行分类。与模糊聚类平均算法相比,基于规则的模糊逻辑分类可确保获得更好的结果,因为在这种情况下可以逐步提高分类结果。通过使用神经网络对裂纹信号进行分类,拥有良好的“学习模式”非常重要。延时网络在此应用中的优势在于,该网络可以“学习”涡流时间信号。不需要信号预处理。 !9

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