This work investigates the application of extreme learning machine, a fast training neural network model, for an ultrasound nondestructive evaluation decision support system. A novel segmented analysis of time-of-flight diffraction ultrasound signals is proposed in order to produce high flaw detection efficiency and low computational requirements, making it possible to be used in embedded applications. The frequency contents of TOFD signals temporal segments, estimated using the discrete Fourier transform, were used to feed the classification system. The test objects consisted of a set of SAE 1020 welded carbon steel plates, in which occur four types of defects. The obtained experimental results indicate that the proposed method is able to combine high accuracy, fast training and full exploration of the TOFD signal information.

中文翻译：

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使用极限学习机对用于焊接钢板缺陷检测的飞行时间衍射超声进行分段分析

这项工作研究了极限学习机（一种快速训练神经网络模型）在超声无损评估决策支持系统中的应用。提出了一种新颖的飞行时间衍射超声信号分段分析，以产生高缺陷检测效率和低计算要求，使其可用于嵌入式应用。使用离散傅立叶变换估计的 TOFD 信号时间段的频率内容用于提供分类系统。测试对象由一组 SAE 1020 焊接碳钢板组成，其中出现四种类型的缺陷。实验结果表明，所提出的方法能够将高精度、快速训练和对TOFD信号信息的充分探索相结合。