Research on alternating current field measurement defect identification method based on BiLSTM-Attention

This study addresses the issues of high signal interpretation complexity and low efficiency in manual defect identification associated with alternating current field measurement (ACFM) technology in practical industrial inspections. A defect identification method that integrates a bidirectional long short-term memory (BiLSTM) network with an attention mechanism is proposed. By constructing a hybrid model with the capability for temporal feature selection, the method enhances the extraction of subtle defect features from ACFM signals. The experimental results show that on a test set containing 500 multi-condition signals (covering defects in 20# steel plates with depths ranging from 0.5 mm to 3 mm and lengths from 10 mm to 50 mm), the model achieves an accuracy rate of 92.10% ± 1.2%. Compared to traditional manual inspection, the average single determination time is reduced to 17.14 ms (an efficiency improvement of approximately 68%), with the smallest reliably identifiable defect size being 0.5 mm (depth) × 10 mm (length). Compared to the baseline long short-term memory (LSTM) model, the F1 score improves by 10.07 percentage points to 94.65% and the memory usage of the model is 2.28 MB, meeting the deployment requirements for industrial embedded devices. This method provides a new technical pathway for the automation of ACFM inspections.