Novel wavelet self-optimisation threshold denoising method in axle press-fit ultrasonic defect detection

Axles are one of the most important parts of railway locomotives and vehicles. The periodic ultrasonic inspection of axles can effectively detect and monitor axle fatigue cracks. However, in the wheel-seat press-fit zone, the complex interface contact condition reduces the signal-to-noise ratio (SNR). Therefore, the probability of false positives and false negatives increases. A novel wavelet threshold function is created to remove noise and suppress press-fit interface echoes in ultrasonic axle defect detection. Self-optimisation of the function is proposed using the positive correlation between the correlation coefficient and SNR. The performance of the proposed method is assessed using real data and compared with traditional threshold methods and a state-of-the-art threshold. The statistic results of the amplitude and peak SNR of defect echoes show that the proposed self-optimisation of the wavelet threshold denoising method not only maintains the amplitude of defect echoes but also provides a higher peak SNR.