Guided Wave Inspection for Bottom Edge of Rails

Hayashi, Takahiro; Miyazaki, Yusuke; Murase, Morimasa; Abe, Tsukasa

doi:10.1063/1.2717970

Cited by 18 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, conventional nondestructive evaluation (NDE) methods are known to be difficult to apply for the foot area of the switch rail. [2][3][4][5] In addition, the closed operation mode is adopted during the day, 6 while the detection and maintenance of the railway can only be performed at night. There is thus little time available for the on-the-spot inspection of the switch rail.…”

mentioning

confidence: 99%

Multi-feature integration and machine learning for guided wave structural health monitoring: Application to switch rail foot

Liu

Tang

et al. 2021

Structural Health Monitoring

View full text Add to dashboard Cite

Switch rails are weak but essential components of high-speed railway systems that have urgent nondestructive testing requirements owing to aging and the associated potential for fatigue damage accumulation. This study presents a multi-feature integration and automatic classification algorithm for switch rail damage using guided wave monitoring signals. A combination of piezoelectric transducers and magnetostrictive patch transducers is adopted to improve the monitoring performance and meet actual monitoring requirements. Furthermore, multiple features extracted from various signal processing domains—such as the time domain, power spectrum domain, and time–frequency domain—are proposed and defined according to the structure and characteristics of the switch rail and guided wave to represent the complex nature of the damage. A damage index is defined to eliminate the influence of various environmental and operational conditions, signal power, and other factors. In addition, a feature selection method based on binary particle swarm optimization with a new fitness function is proposed to select the most damage-sensitive features and eliminate irrelevant and redundant features to improve the classification performance. Moreover, considering that the results are easily influenced by experts’ subjective judgment and experience, the least-squares support-vector machine is used to construct automatic classification models to reduce the probability of artificial incorrect diagnosis and improve the generalization ability to unknown environments. Finally, three types of experiments on the foot of a switch rail are presented to evaluate the proposed method. The results indicate that the proposed method is capable of identifying damage in challenging cases and is superior to conventional methods.

show abstract

mentioning

confidence: 99%

Multi-feature integration and machine learning for guided wave structural health monitoring: Application to switch rail foot

Liu

Tang

et al. 2021

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…From Figure 11 we can see, the result of 2D-FFT coincides well with the frequency wavenumber curve of the corresponding mode and there is no interference mode. The wavenumber ξ and frequency f are substituted into Equation (10). The calculation results of simulated phase velocities are shown in Table 4, and compared with theoretical values.…”

Section: Mode Enhancement Simulation Resultsmentioning

confidence: 99%

“…In the research of rail defect detection based on guided waves, some scholars selected the suitable modes for rail defect detection by a mode classification method. Hayashi [10] divided the modes of the rail base into three categories. By analyzing the dispersion characteristics of guided waves, the concept of dominant modes is proposed.…”

Section: Introductionmentioning

confidence: 99%

An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection

Shi

Zhang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Different guided wave mode has different sensitivity to the defects of rail head, rail web and rail base in the detection of rail defects using ultrasonic guided wave. A novel guided wave mode selection and excitation method is proposed, which is effective for detection and positioning of the three parts of rail defects. Firstly, the mode shape data in a CHN60 rail is obtained at the frequency of 35 kHz based on SAFE method. The guided wave modes are selected, combining the strain energy distribution diagrams with the phase velocity dispersion curves of modes, which are sensitive to the defects of the rail head, rail web and rail base. Then, the optimal excitation direction and excitation node of the modes are calculated with the mode shape matrix. Phase control and time delay technology are employed to achieve the expected modes enhancement and interferential modes suppression. Finally, ANSYS is used to excite the specific modes and detect defects in different rail parts to validate the proposed methods. The results show that the expected modes are well acquired. The selected specific modes are sensitive to the defects of different positions and the positioning error is small enough for the maintenance staff to accept.

show abstract

“…Rail Dispersion is a specially developed software for calculations of rail dispersion curves and wave structures, as shown in Figure 3. 101 Cross sections of various shapes can be drawn by the CAD and input in the Rail Dispersion as a DXF file. Then, GiD software can be adopted to generate the cross-section mesh.…”

Section: Dispersive Characteristics Of Guided Waves In Railmentioning

confidence: 99%

Guided wave–based rail flaw detection technologies: state-of-the-art review

Huat

Koh

et al. 2021

Structural Health Monitoring

View full text Add to dashboard Cite

The unavoidable increase in train speed and load, as well as the aging of railway facilities, is requiring more and more attention to rail defects detection. As a promising tool for rail, in-service high-speed inspection, guided wave–based detection technologies have been developed in succession by researches in the past two decades. However, there is a lack of a systematic review on the developments and performances of these technologies. This article reviews ultrasonic rail inspection methods comprehensively with the focus on the state-of-the-art technologies based on guided wave. Different excitation options, including train wheel, electromagnetic acoustic transducer, pulsed laser, air-coupled, and contact piezoelectric transducer, are described, respectively, along with their inspection sensitivities, regions, and potential speeds. Finally, future challenges and prospects are discussed to a certain extent to provide references for researchers in this area.

show abstract

Guided Wave Inspection for Bottom Edge of Rails

Cited by 18 publications

References 0 publications

Multi-feature integration and machine learning for guided wave structural health monitoring: Application to switch rail foot

Multi-feature integration and machine learning for guided wave structural health monitoring: Application to switch rail foot

An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection

Guided wave–based rail flaw detection technologies: state-of-the-art review

Contact Info

Product

Resources

About