Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

Chinese Journal of Mechanical Engineering

Original Article

Zhonghua Ni¹  Hui Zhang¹  Shengkang Zong¹  Sheng Wang¹  Zhitao Luo¹  Xinkai Wu¹ 

[1] Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, 211189, Nanjing, China;

关键词: Ultrasonic guided waves;    Singular value decomposition;    Damage detection and localization;    Environmental and operational conditions;    One-dimensional convolutional neural network;   

DOI  :  10.1186/s10033-023-00889-3

 received in 2022-01-20, accepted in 2023-04-18,  发布年份 2023

来源: Springer

【 摘 要 】

Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions (EOC), which are inevitable in the normal inspection of civil and mechanical structures. This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network (1D-CNN). After singular value decomposition-based feature extraction processing, a temporal robust damage index (TRDI) is extracted, and the effect of EOCs is well removed. Hence, even for the signals with a very large temperature-varying range and low signal-to-noise ratios (SNRs), the final damage detection and localization accuracy retain perfect 100%. Verifications are conducted on two different experimental datasets. The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises, and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20°C to 60°C. It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly, showing great potential for application in complex and unknown EOC.

【 授权许可】

CC BY   
© The Author(s) 2023

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