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Robust damage detection and localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

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Chinese Journal of Mechanical Engineering 2023年第3期36卷 252-261页

作者： Shengkang Zong Sheng Wang Zhitao Luo Xinkai Wu Hui Zhang Zhonghua Ni Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments School of Mechanical EngineeringSoutheast UniversityNanjing 211189China

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 *** 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 ***,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 *** 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℃to 60℃.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.

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

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