In order to monitor the rail base, the dispersion characteristics and propagation properties of the guided wave are studied. Firstly, two modes named as Modes V1 and V2 are selected by the semianalytical finite element method (SAFE). The region at the bottom edge can be monitored by Mode V1, while the junction of the base edge and the flange can be detected by Mode V2. Then, the characteristics in the propagation process are analyzed using the finite element method (FEM). The two modes can be separated about 0.6 ms after they are excited. Thirdly, a wave attenuation algorithm based on mean is proposed to quantify the wave attenuation. Both waves can have weak attenuation and be detected within 5 m. Finally, a mode-identified experiment is performed to validate the aforementioned analysis. And a defect detection experiment is performed to demonstrate the excellent monitoring characteristics using Mode V2. These results can be used to monitor the rail base in practice engineering.
In conventional Lamb wave-based damage detection, estimation is generally performed using the difference between the measured and baseline signals. However, it is difficult to maintain consistent measurement conditions between the measured and baseline signals, likely leading to large measurement errors. This paper proposed a baseline-free Lamb wave-based detection method for locating damage to structures. The damage scattering waves are extracted according to their features in the frequency-wavenumber spectrum and the damage locations are visualized using probability imaging. To reduce the complications of full wavefield acquisition, wavefield data are acquired using piezoelectric transducer (PZT) arrays at sampling rates much lower than the Nyquist frequency, and the original wavefield is reconstructed by applying compressed sensing. Experimental results on an aluminum plate indicate that the proposed method can provide the damage probability at each plate position without requiring a reference signal, suggesting its applicability to damage diagnosis of structures. Moreover, the proposed method achieves a compression ratio of 86.7% compared with the use of Nyquist sampling.
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