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

Abstract

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

Automatic Judgment of Ultrasonic Detection for Solid Axle Inlaid Part

“…Filter the detail coefficients before reconstruction by wavelet self-optimization threshold function [11], as shown in equation (1).…”

Laser ultrasonics for defect evaluation on coated railway axles

“…Thus, one of the main aims of the Euraxles research project [23] has become minimizing the risk of axle failure either by improving available nondestructive techniques, or by developing new ones also. The project mainly focused on ultrasonic techniques (UT) [24], which are the non-destructive techniques most widely used in the railway field [25][26][27][28][29]. In this regard, when visual testing (VT) reveals small defects on the surface, such as paint damage (peeling) or corrosion pits, magnetic particles testing (MT) and ultrasonic testing (UT) are the recommended inspection techniques, according to the adopted maintenance standard procedure [30].…”

Ultrasonic Flaw Echo Enhancement Based on Empirical Mode Decomposition

“…Consequently, flaw echo detection may be challenging. Numerous methods have been proposed to enhance flaw echoes, such as split spectrum processing [1,2,3,4], wavelet transforms [5,6,7,8,9,10], the Stockwell transform [11,12,13,14], and empirical mode decomposition (EMD) [15,16,17,18,19,20,21,22] (including the so-called ensemble EMD, i.e., EEMD [23]).…”