@article{ART003383481},
author={Jeong-Beom Hong and Byung-Chul Ko},
title={A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2026},
volume={31},
number={9},
pages={1-14}
TY - JOUR
AU - Jeong-Beom Hong
AU - Byung-Chul Ko
TI - A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding
JO - Journal of The Korea Society of Computer and Information
PY - 2026
VL - 31
IS - 9
PB - The Korean Society Of Computer And Information
SP - 1
EP - 14
SN - 1598-849X
AB - Laser welding applies a high-energy-density beam to the material, involving complex physical phenomena, and the measured optical signals exhibit non-stationary characteristics. Defect-induced signal variations appear not only as amplitude changes but also as changes in frequency composition and baseline shifts, and they rarely form fixed patterns corresponding one-to-one with defect types. Accordingly, a model is required that can be trained using only normal data while jointly learning local time-domain patterns and time–frequency characteristics.
The proposed model represents both local temporal patterns and time–frequency variations through a hybrid time–frequency embedding combining a multi-scale dilated 1D convolution branch with a Morlet wavelet-based wavelet transform branch, and learns global dependencies among time steps via temporal self-attention. The anomaly detection performance was evaluated through synthetic anomaly injection experiments using various condition combinations and varying anomaly intensities. For actual defects, the model achieved AUROC of 0.925 and 0.941, AUPRC of 0.923 and 0.931, and recall of 0.901 and 0.912 for localized over-welding, corresponding to localized signal variation, and gap defects, corresponding to signal variation over a wide interval, respectively.
KW - Laser welding;Transformer;Wavelet transform;Anomaly Detection;Non-stationary signal;Photodiode signal
DO -
UR -
ER -
Jeong-Beom Hong and Byung-Chul Ko. (2026). A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding. Journal of The Korea Society of Computer and Information, 31(9), 1-14.
Jeong-Beom Hong and Byung-Chul Ko. 2026, "A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding", Journal of The Korea Society of Computer and Information, vol.31, no.9 pp.1-14.
Jeong-Beom Hong, Byung-Chul Ko "A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding" Journal of The Korea Society of Computer and Information 31.9 pp.1-14 (2026) : 1.
Jeong-Beom Hong, Byung-Chul Ko. A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding. 2026; 31(9), 1-14.
Jeong-Beom Hong and Byung-Chul Ko. "A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding" Journal of The Korea Society of Computer and Information 31, no.9 (2026) : 1-14.
Jeong-Beom Hong; Byung-Chul Ko. A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding. Journal of The Korea Society of Computer and Information, 31(9), 1-14.
Jeong-Beom Hong; Byung-Chul Ko. A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding. Journal of The Korea Society of Computer and Information. 2026; 31(9) 1-14.
Jeong-Beom Hong, Byung-Chul Ko. A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding. 2026; 31(9), 1-14.
Jeong-Beom Hong and Byung-Chul Ko. "A Wavelet-Enhanced Temporal Attention Network for Unsupervised Anomaly Detection in EV Battery Tab-to-Tab Laser Welding" Journal of The Korea Society of Computer and Information 31, no.9 (2026) : 1-14.
