Fatigue crack sizing in rail steel using crack closure-induced acoustic emission waves

Abstract

The acoustic emission (AE) technique is a promising approach for detecting and locating fatigue cracks in metallic structures such as rail tracks. However, it is still a challenge to quantify the crack size accurately using this technique. AE waves can be generated by either crack propagation (CP) or crack closure (CC) processes and classification of these two types of AE waves is necessary to obtain more reliable crack sizing results. As the pre-processing step, an index based on wavelet power (WP) of AE signal is initially established in this paper in order to distinguish between the CC-induced AE waves and their CP-induced counterparts. Here, information embedded within the AE signal was used to perform the AE wave classification, which is preferred to the use of real-time load information, typically adopted in other studies. With the proposed approach, it renders the AE technique more amenable to practical implementation. Following the AE wave classification, a novel method to quantify the fatigue crack length was developed by taking advantage of the CC-induced AE waves, the count rate of which was observed to be positively correlated with the crack length. The crack length was subsequently determined using an empirical model derived from the AE data acquired during the fatigue tests of the rail steel specimens. The performance of the proposed method was validated by experimental data and compared with that of the traditional crack sizing method, which is based on CP-induced AE waves. As a significant advantage over other AE crack sizing methods, the proposed novel method is able to estimate the crack length without prior knowledge of the initial crack length, integration of AE data or real-time load amplitude. It is thus applicable to the health monitoring of both new and existing structures.

Rail crack monitoring based on Tsallis synchrosqueezed wavelet entropy of acoustic emission signals: A field study

“…Two examples are given in Figures 18 and 19, where the SWTs were plotted in the characteristic frequency band of interest [200–700] kHz. Similar with the wavelet analysis results of crack propagation–induced AE waves obtained from the laboratory fatigue tests on rail steel specimens as reported in Li et al, 10 the AE transient in Figure 18 had prominent energy distributed in both the lower frequency band [200–350] kHz and the higher frequency band [350–700] kHz. Such type of AE transients was therefore considered to be induced by crack propagation process happened in the rail.…”

“…Kostryzhev et al 9 characterized the power spectra of different AE waves due to plastic deformation, brittle crack growth, and ductile crack growth. Li et al 10 proposed an index based on wavelet power of AE signals to distinguish between the crack propagation–induced AE waves and their crack closure–induced counterparts, and a novel crack sizing method by taking advantage of the crack closure–induced AE waves. On the other hand, several small-scale laboratory test rigs were set up to simulate wheel–rail interaction and to investigate the performance of AE technique.…”

Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network

“…By fatigue tests on rail steel specimens, AE count rate or other indexes were developed to identify the onset and size of cracks. [11][12][13] Through small-scale test rigs simulating the wheel-rail interaction and pencil lead break (PLB) simulating AE activities induced by rail defects, various algorithms were investigated to denoise AE signals and to detect rail defects. [14][15][16][17][18] A few field studies have been done recently, which take into account high-operational noise of railway and realistic complex cracking conditions.…”

“…For instance, several failure mechanisms of corroded galvanized steel were identified by wavelet power, entropy, and bispectrum; 22 signals generated by crack propagation and crack closure were separated by a wavelet power-based index and used for crack sizing of the fatigue specimens. 13 Since a large amount of data with high noise and uncertainty would be collected in the railway field, it is difficult to develop a simple index to efficiently classify them. Machine learning algorithms have, therefore, been introduced.…”

Monitoring of Fatigue Crack Propagation by Damage Index of Ultrasonic Guided Waves Calculated by Various Acoustic Features

“…More research studies of temporal signal features primarily focus on the magnitude-based and energy-based signals [14,15], wave reflections or transmissions [16,17], energy dissipation [18], and mode conversions [19]. Meanwhile, acoustic emission (AE) technology is widely used for continuously monitoring fatigue cracks, and AE-based fatigue crack evaluation techniques are exploited based on counting the number of signals allured by crack propagation [20,21]. The scattering and attenuation of ultrasonic guided waves caused by fatigue cracks are applied to quantify the cracks' growth [22][23][24].…”