A dilated convolution-based method with time series fine tuning for data-driven crack length estimation

doi:10.1111/ffe.14305

A dilated convolution-based method with time series fine tuning for data-driven crack length estimation

2024-04-15

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DOI：10.1111/ffe.14305

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This paper presents a novel crack size estimation method based on dilated convolution and time series fine tuning using in-situ Lamb wave. The contributions of this proposed method are twofold: (1) a dilated convolution-based neural network which is an effective representation learning methods with larger receptive fields for periodic signals and (2) a crack length adjustment approach based on previous estimations with a time series fine tuning method. The time series fine tuning method utilizes the monotonicity of the crack length with respect to time. A new evaluation metric has been adopted, which considers prediction errors, time errors, and monotonicity errors. The proposed method outperforms the previous feature extraction-based methods with or without time series fine tuning in this new evaluation metric. Crack estimation method using deep learning and time series fine tuning was proposed. The neural network using a combination of dilated convolution and long short-term memory (LSTM) network was designed. The proposed method outperformed prior manual feature extraction-based approaches.

Keywords:

deep learning

dilated convolution

Lamb wave

neural networks

time series fine tuning

A dilated convolution-based method with time series fine tuning for data-driven crack length estimation

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