In an attempt to analyze the feasibility of a rail monitoring scheme with a wheel-mounted acoustic emission measurement method, a two-dimensional analytical spring model was proposed to interpret the interactions between acoustic waves and stationary wheel-rail contact interfaces. The spring model represents the coupling strength of the interfaces with their stiffness. It accommodates the environmental information of contact stiffness and the acoustic source information, such as the feature frequency and the incident angle in the analysis of interface transmissibility. The fractal dimension of the interface is introduced into the spring model to interpret the effect of the axle load on the interface stiffness and the variation in transmissibility. Discrete Rayleigh integration is further combined to obtain the directivity of the interface and estimate the acoustic field intensity in the entire wheel. A wheel-rail contact rig was designed to simulate the actual contact conditions in a railway. Experimental data acquired from this test rig were utilized to validate the model, with regard to the interfacial stiffness in normal incidence and amplitudes of the transmitted waves in oblique incidence. After verifying the reliability of the model, the acoustic field intensity in the wheel was visualized under the assumed environmental conditions. Finally, a discussion is presented to determine a proper angular separation for the wheel-mounted sensor system, and substantiate the feasibility of the system.
Multi-feature fusion has been widely used to enhance recognition accuracy for different health stages of rail, which may lead to high-dimensionality and information redundancy of signal. In addition, conventional supervised methods require plenty of labeled samples with class information, which can lead to significant time and economic costs. In order to improve the effectiveness of the electromagnetic acoustic emission (EMAE) technique in rail crack defect recognition, a novel method including multi-feature fusion based on weakly supervised learning and recognition threshold construction, is proposed in this paper. First, a mechanism contains of multi-feature extraction and feature selection, is developed to fully reflect the information of different health stages of rail and avoid interference caused by the ineffective features. Then, the effective features and a novel weakly unsupervised label are input into the self-normalizing convolutional neural network and long short-term memory (SCNN-LSTM) model to construct the rail health indicator (RHI). Finally, the recognition threshold is calculated by the characteristics of RHI, to achieve crack recognition automatically. Furthermore, the experimental results under different working conditions demonstrate that the proposed method achieves higher recognition performance than other existing methods in rail crack defect recognition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.