LAUSR

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.