Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network

Joint Authors

Peng, Jie
Liu, Baoling
Yuan, Xiaocui
Hu, Huiling
Zeng, Xuan
Zhu, Zhifang
He, Jun

Source

Complexity

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-11-24

Country of Publication

Egypt

No. of Pages

18

Main Subjects

Philosophy

Abstract EN

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.

American Psychological Association (APA)

Liu, Baoling& He, Jun& Yuan, Xiaocui& Hu, Huiling& Zeng, Xuan& Zhu, Zhifang…[et al.]. 2020. Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network. Complexity،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1145607

Modern Language Association (MLA)

Liu, Baoling…[et al.]. Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network. Complexity No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1145607

American Medical Association (AMA)

Liu, Baoling& He, Jun& Yuan, Xiaocui& Hu, Huiling& Zeng, Xuan& Zhu, Zhifang…[et al.]. Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network. Complexity. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1145607

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1145607