Localizing damage on stainless steel structures using acoustic emission signals and weighted ensemble regression-based convolutional neural network - Ai, Li; Bayat, Mahmoud; Ziehl, Paul (Measurement : journal of the International Measurement Confederation, 2023)
•A data fusion method is presented, which integrates AE waveform, FFT spectrum, and spectral entropy.•A weighted ensemble regression-based CNN was proposed to estimate the coordinates of damage.•Fusion data, CWT, and STFT coefficients were used as input data.•The proposed model estimates the coordinates of damages with high performance. Nuclear power generation is an essential part of the electrical supply in the United States, and it is an effective way to achieve low carbon power generation. Nuclear power generation produces spent radioactive fuel. If improperly disposed of or improperly stored, spent fuel can affect the environment and human health. Currently, in the United States, spent fuel is typically stored in stainless steel canisters. Some stainless steel canisters deployed in coastal areas are subject to stress corrosion cracks. Long-term monitoring and to provide timely maintenance of the storage canisters is necessary to prevent leakage of spent fuel due to damaged canisters. Acoustic emission (AE) is a structural health monitoring (SHM) technique that can be utilized to monitor large-scale metallic structures because it is extremely sensitive to damage initiation and propagation in materials. However, the challenge in using AE is in deploying a minimal number of AE sensors on a canister due to cost and environmental restrictions while still being able to precisely detect and localize damage formation. The innovation of this paper lies in the development of an automated damage localization method to estimate the coordinates of damage by using a single AE sensor. A data fusion approach was designed to integrate the information from waveforms, fast Fourier transform (FFT) spectrums, and spectrum entropy and then convert the AE signals into three types of images along with short-time Fourier transform (STFT) and continuous wavelet transforms (CWT). A weighted ensemble regression-based convolutional neural network was proposed to analyze the images and compute the coordinates of damage. The proposed method was validated on a large-scale steel plate specimen that simulate the canister, and three-fold cross-validation was conducted to ensure the method was effectively evaluated. The results suggest that the proposed method has a high performance rate for locating damage.