Reference-free fatigue crack detection using deep long short-term memory network (DLSTM) and nonlinear ultrasonic modulation

Nonlinear ultrasonic modulation is sensitive to fatigue crack, but a reference signal or user-specified threshold is often required for crack diagnosis, easily causing false alarms in noisy environments. In this study, a reference-free damage detection method was developed by applying a deep long short-term memory network (DLSTM) to nonlinear ultrasonic modulation signals. First, an ultrasonic signal was generated and measured using piezo-ceramic ultrasonic transducers. Subsequently, a DLSTM network was constructed and trained to learn the inherent sequential patterns of the measured ultrasonic signals. Then, an absolute damage index (ADI) was defined and computed using only the current ultrasonic signal without any reference ultrasonic signal obtained from the intact condition. Finally, the crack was automatically detected using the ADI and without any user-specified threshold. The performance of the proposed method was examined using data from a submerged floating tunnel model and an actual long-span bridge. The results highlight the feasibility of the proposed method for automatic fatigue crack detection.

Automated summary

In this study, a reference-free damage detection method was developed by applying a deep long short-term memory network (DLSTM) to nonlinear ultrasonic modulation signals. The proposed method avoids the need for a reference signal or user-specified threshold, thus eliminating false alarms in noisy environments. The results highlight the feasibility of the proposed method for automatic fatigue crack detection.