Fracture failure analysis of pressure gauge bolt on fuel gas system of offshore platform

Qin, Chudong; He, Shi; Zhong, Longfeng; Feng, Wei; Pang, Jinshan; Li, Qiuqiu; He, Weichu

doi:10.1016/j.engfailanal.2020.104959

Cited by 3 publications

(1 citation statement)

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“…Critical components often operate in harsh environments and are subjected to cyclic stress and strain in specialized applications, such as aerospace engines, offshore platforms, and other similar scenarios, making them susceptible to the formation of micro-cracks and other defects on or near the surface [1][2][3]. If these defects are not detected in a timely manner, they can rapidly propagate and lead to the sudden catastrophic failure of the entire structure [4,5].…”

Section: Introductionmentioning

confidence: 99%

Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network

Fei,

Cao,

et al. 2023

Applied Sciences

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This paper proposes a method for the detection and depth assessment of tiny defects in or near surfaces by combining laser ultrasonics with convolutional neural networks (CNNs). The innovation in this study lies in several key aspects. Firstly, a comprehensive analysis of changes in ultrasonic signal characteristics caused by variations in defect depth is conducted in both the time and frequency domains, based on discrete frequency spectra and original A—scan signals. Continuous wavelet transform (CWT) is employed to obtain wavelet time–frequency maps, demonstrating the consistent characteristics of this image with crack depth variations. A crucial innovation in this research involves the targeted design and optimization of the model based on the characteristics of ultrasonic signals and dataset size. This includes aspects such as data preparation, CNN architecture construction, and hyperparameter selection. The model is tested using a random validation set, which effectively demonstrates the CNN model’s validity and high precision. The proposed method enables the recognition and depth assessment of tiny defects on or near surfaces.

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