Application Cases

Introduction

Ultrasonic guided-wave technology has gained extensive attention for structural health monitoring and non-destructive testing.Nevertheless, complex cross-sectional waveguides such as railway rails generate abundant guided-wave modes, which readily cause wave-packet superposition and make it difficult to isolate individual modes and identify reflected defect echoes.Existing mode-separation approaches mostly target simple plate- or pipe-shaped components and require dense sensor arrays, restricting their practical deployment for rail inspection.Consequently, railway engineering urgently demands feasible techniques to realize multimode decomposition and defect identification using only a small number of receiving sensors for rail non-destructive evaluation.

The simulation and experimental work presented in this paper addresses the above-mentioned practical needs in rail non-destructive testing.

The authors proposed a sparse-point-array-based mode wave-packet separation algorithm and constructed a complete defect-detection workflow.

With merely 24 receiving sensors arranged entirely on the rail web, both numerical simulation and laboratory experiments achieved cross-sectional localization of defects on the rail head, rail web and rail base, alongside identification of defect size and longitudinal position.These experimental results validate that the proposed method can overcome the limitation of densesensor requirements, offering a feasible technical reference for real-world railway track structural health monitoring and guided-wave-based non-destructive inspection of other complex waveguide structures.

Research Direction

Elastic Wave-Based Rail Defect Detection

Experimental objective

Structures with irregular and complex cross-sections exhibit numerous elastic wave modes, making it difficult to separate individual mode wave packets and identify reflected echoes during defect detection.

By inputting high-energy elastic wave signals through a power amplifier and acquiring elastic wave signals on a sparse array, a mode separation formula based on the sparse array was derived using the pseudo-inverse algorithm.

With all receiving array elements arranged on the rail web, defects in the rail head, rail web, and rail foot were successfully identified.

Testing equipment

Arbitrary waveform generator, power amplifier (Aigtek ATA-2022H), oscilloscope, piezoelectric ceramic sensors, etc.

Experimental process

First, a healthy 2 m-long rail was selected to obtain its dispersion information.

In this experiment, an arbitrary waveform generator, a power amplifier (Aigtek ATA-2022H), and an oscilloscope were used to generate, amplify, and extract elastic wave signals.

A bolt hole was created at the rail web to simulate a rail web defect, located 1 m from the end face.

The sparse receiving array consists of 24 points with a spacing of 40 mm between adjacent points.

The excitation sensor provides a stable elastic wave signal input with a center frequency of 15 kHz, using a 20-cycle Hanning-windowed signal.

During the experiment, the receiving sensor was moved sequentially to extract signals at each point of the sparse receiving array.

The actual frequency response matrix was constructed, and separation of multiple mode wave packets was achieved based on the sparse array wave packet separation principle.

Figure1 Experimental installations.

Figure2 Sensors Installation

Experimental results

When defects are located at different positions on the rail cross-section, each mode produces different echo characteristics due to differences in the wave structure intensity distribution, allowing the position of the defect on the cross-section to be identified from the reflected wave response.

As the defect gradually increases in size, the intensity of the reflected echo of each mode increases accordingly, and the relative intensity between modes also changes regularly according to the distribution characteristics of the wave structure.

In the mode reflected wave packet diagram, only Mode 3 shows a clear reflected wave packet, while the reflected amplitudes of the remaining modes are relatively small.

The existence of the rail defect can be identified from the reflected wave packet results, and the different responses of each mode's reflected wave packet can be used to preliminarily determine that the defect is located at the rail web.

Therefore, the experiment successfully demonstrates the correctness of the sparse array-based wave packet separation method.

Figure3 Test resultsA

Figure4 Test resultsB

Figure5 Test resultsC

The effectiveness of the amplifier in this experiment

Excitation signal generation, forming high-energy ultrasonic guided waves

Application fields

Rail defect detection, ultrasonic guided wave, structural health monitoring, nondestructive testing, guidedwave mode separation, sparse point array, waveguide structure inspection