Abstract
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.
Highlights
Renewable energy source such as wind and tides are gradually replacing coal and oil as new energy sources because of their inexhaustible and pollution-free advantages.e large in-service equipment applied in the renewable energy system such as supports and pipes often suffers from various defects due to the harsh working environment, which is dangerous for the safe operation of the renewable energy system
The parameters and the intermediate variables of deep convolutional network (DCNN) need too much memory and computational effort [22], which restricts the application of DCNN to nondestructive testing, including pulsed eddy current testing (PECT), because the inspection and maintenance of large-scale in-service equipment in the field of renewable energy source is often carried out using portable NDT equipment within a given time, and small memory and fast speed are as important as the accuracy for the signal processing methods
E result of defect identification is shown in Figure 4. e optimizer used by five DCNNs was stochastic gradient descent with momentum (SGDM), whose main parameters, i.e., momentum, batch size, initial learning rate, and training epochs, were set as 0.9, 4, 0.0001, and 20, respectively. e loss function was the crossentropy function
Summary
Renewable energy source such as wind and tides are gradually replacing coal and oil as new energy sources because of their inexhaustible and pollution-free advantages. The parameters and the intermediate variables of DCNN need too much memory and computational effort [22], which restricts the application of DCNN to nondestructive testing, including PECT, because the inspection and maintenance of large-scale in-service equipment in the field of renewable energy source is often carried out using portable NDT equipment within a given time, and small memory and fast speed are as important as the accuracy for the signal processing methods. In order to avoid the difficulty of manual feature extraction and overcome the shortcomings of DCNN that require large memory and computational cost, we propose a defect recognition pipeline based on PECT signal and an optimized DCNN to intelligently, quickly, and accurately identify defect. A novel compound method is proposed to optimize VGG16 baseline architecture to obtain optimized DCNN which is used for feature extraction and pattern recognition of 2D TFRs. e pipeline was verified by PECT experiment considering such interference as lift-off and noise in terms of accuracy and computational cost.
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