Feasibility investigation of rail monitoring by wheel-mounted acoustic emission technology using two-dimensional analytical contact acoustic nonlinearity model

Abstract

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.

Defect detection of ferromagnetic rail using EMAE-based peak-to-peak method and confidence probability indicator

“…Besides, the excitation current of the EMAE was set to sinusoidal signal with a frequency of 1 MHz. Recently researches had shown that the defect AE signals were mainly concentrated in [0.1 MHz, 1 MHz], but the AE signals will be attenuated after 0.8 MHz [30,31]. Therefore, in general, the mainly frequency distribution of AE signals was below 1 MHz, the excitation frequency was set to 1 MHz to avoid signal overlapping.…”

“…Device 6 were two receiving sensors (Model: VS900-RIC) and used to utilized to receive the signals from the specimen. Besides, the couplant was filled between the receiving sensors and the specimen, to reduce acoustic reflection and refraction [31]. The AE signals were acquired by the AE acquisition system (Device 8, Model: Vallen AMSY-6) with a sampling rate of 5 MHz.…”

An improved rail crack detection method based on the wavelet subband LMS adaptive filtering and wavelet analysis

“…Considering its high sensitivity and dynamic detection without blind regions, AE technique has received considerable attention by the research community in the application of rail crack detection [5]. In such scenarios, AE transducers are generally attached to the rail surfaces and directly receive the transient bursts emitted from damaged materials, which contain abundant information associated with the growing cracks, such as the feature frequency characteristics [6]. However, the crack growth typically occurs when the trains are passing and the recorded crack signals are interfered with the strong mechanical noises from the wheel-rail interactions.…”

Rail crack defect recognition based on a multi-feature fusion algorithm using electromagnetic acoustic emission technique

“…Over the past 20 years, high-speed railway systems have made significant progress in the field of freight transport and passenger travel [1]. Meanwhile, with the heavy operation of the railway system, rail crack defects caused by the rolling contact fatigue are becoming more and more common [2].…”