Accurate and rapid defect identification based on pulsed eddy current testing (PECT) plays an important role in the structural integrity and health monitoring (SIHM) of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network (DCNN), such as large memory and high computational cost, an intelligent defect recognition pipeline based on the general Warblet transform (GWT) method and optimized two-dimensional (2-D) DCNN is proposed. The GWT method is used to convert the one-dimensional (1-D) PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer (BN) after every convolutional layer(Conv) and fully connection layer (FC), simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing (PECT) experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation (TFR) obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too.

中文翻译：

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基于PECT信号和优化的二维深度卷积网络的智能缺陷识别

基于脉冲涡流测试（PECT）的准确，快速的缺陷识别在可再生能源系统中在役设备的结构完整性和健康监测（SIHM）中起着重要作用。然而，在常规的数据驱动的缺陷识别方法中，信号特征提取是耗时的并且需要专家经验。为避免手动特征提取的困难，并克服了传统的深度卷积网络（DCNN）的缺点（例如，内存大，计算量大），基于通用Warblet变换（GWT）方法并优化了两步的智能缺陷识别管道提出了二维（2-D）DCNN。GWT方法用于将一维（1-D）PECT信号转换为用作2D DCNN输入的2D灰度图像。提出了一种复合方法来优化基线VGG16（一种著名的DCNN），它从以下四个方面进行优化：减小输入大小，在每个卷积层（Conv）和完全连接层（FC）之后添加批处理归一化层（BN），简化FC，并在Convs中删除不重要的过滤器，以减少内存和计算成本，同时提高准确性。通过考虑干扰因素（包括剥离和噪声）的脉冲涡流测试（PECT）实验，可以得出以下结论。通过GWT方法获得的时频表示（TFR）不仅具有出色的瞬态分量分析能力，而且受图像尺寸减小的影响较小；提出的优化DCNN可以准确识别缺陷类型，而无需人工提取特征。与基线VGG16相比，优化的DCNN获得的精度提高了7％，达到了约99.58％，并且内存和计算成本降低了98％。而且，与其他知名的DCNN（例如GoogLeNet，Inception V3，ResNet50和AlexNet）相比，优化后的网络在准确性和计算成本方面也具有明显的优势。