A novel laser-based duffing oscillator system to identify weak ultrasonic guided wave signals related to rail defects

Ng, Kim Ming; Ghafoor, Imran; Tse, Peter W.

doi:10.1016/j.optlaseng.2022.107111

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…Therefore, differential processing By comparing the signal for a healthy rail with that for a crack in the bottom of the rail for the same path, the interaction between the crack and the UGW can be visually highlighted, which is beneficial for signal analysis. Therefore, differential processing was performed for all received UGW signals, and the formula is shown in Equation (9). By comparing the 16 path signals of transverse cracks with different depths, it was found that when the depth of the cracks is small, the PZT-5A on the side of the rail bottom could better characterise the damage state than the one on the upper surface of the rail bottom.…”

Section: Design Of Piezoelectric Sensor Arraymentioning

confidence: 99%

“…Therefore, compared with the NDT method, the monitoring method is more suitable for the At present, non-destructive testing (NDT) technology is usually used for detecting rail damage. Commonly used NDT technologies include visual inspection, magnetic induction, eddy current, photothermal, ultrasonic, etc., [6][7][8][9][10][11]. However, existing NDT methods mainly focus on the inspection of the rail head and rail web, and little research has been conducted on the detection of rail bottom defects, especially for the edge of the rail bottom that is far away from the rail web [12].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Transverse Crack Depth of Rail Bottoms Based on the Ultrasonic Guided Waves of Piezoelectric Sensor Arrays

Yang

Wang

Song

et al. 2022

Sensors

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A method based on the high-frequency ultrasonic guided waves (UGWs) of a piezoelectric sensor array is proposed to monitor the depth of transverse cracks in rail bottoms. Selecting high-frequency UGWs with a center frequency of 350 kHz can enable the monitoring of cracks with a depth of 3.3 mm. The method of arranging piezoelectric sensor arrays on the upper surface and side of the rail bottom is simulated and analyzed, which allows the comprehensive monitoring of transverse cracks at different depths in the rail bottom. The multi-value domain features of the UGW signals are further extracted, and a back propagation neural network (BPNN) is used to establish the evaluation model of the transverse crack depth for the rail bottom. The optimal evaluation model of multi-path combination is reconstructed with the minimum value of the root mean square error (RMSE) as the evaluation standard. After testing and comparison, it was found that each metric of the reconstructed model is significantly better than each individual path; the RMSE is reduced to 0.3762; the coefficient of determination R2 reached 0.9932; the number of individual evaluation values with a relative error of less than 10% and 5% accounted for 100% and 87.50% of the total number of evaluations, respectively.

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Section: Design Of Piezoelectric Sensor Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the Transverse Crack Depth of Rail Bottoms Based on the Ultrasonic Guided Waves of Piezoelectric Sensor Arrays

Yang

Wang

Song

et al. 2022

Sensors

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show abstract

“…Wu [28] carried out simulation and experimental studies on the UGW detection of pipeline defects using the maximum Lyapunov exponent and the Lyapunov fractional dimensions as phase determination indexes, respectively. Ng [29] identified hole defects in rails using the maximum Lyapunov exponent of the Duffing equation. Additionally, Cheng [30] developed a pipeline damage detection method based on the double Duffing equation for detecting weak defect echo signals caused by pipeline defects.…”

Section: Introductionmentioning

confidence: 99%

Rail Flaw Detection via Kolmogorov Entropy of Chaotic Oscillator Based on Ultrasonic Guided Waves

Zeng,

Wu,

Zheng

et al. 2024

Sensors

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Ultrasonic guided wave (UGW) inspection is an emerging non-destructive testing(NDT) technique for rail flaw detection, where weak UGW signals under strong noise backgrounds are difficult to detect. In this study, a UGW signal identification model based on a chaotic oscillator is established. The approach integrates the UGW response into the critical state of the Duffing system to serve as a disturbance control variable. By evaluating the system’s motion state before and after introducing the UGW response, identification of UGW signals can be realized. Thus, the parameters defining the critical state of Duffing oscillators are determined by Ke. Moreover, an electromagnetic transducer was specifically devised to enable unidirectional excitation for UGWs targeted at both the rail base and rail head. Experimental studies showed that the proposed methodology effectively detected and located a 0.46 mm notch at the rail base and a 1.78 mm notch at the rail head. Furthermore, Ke was directly proportional to the notch size, which could be used as a quantitative index to characterize the rail flaw.

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“…2 With the development of non-contact detection technology, manual visual inspection is gradually being replaced. 3 The traditional non-contact inspection methods include eddy current detection, 4 ultrasonic guided wave detection, 5 and infrared detection. 6 However, these methods have limitations, such as being costly, complex, and have limited visual feedback during the defect identification process.…”

Section: Introductionmentioning

confidence: 99%

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

Ding,

Pan,

Zhang

et al. 2023

J. Electron. Imag.

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.Metal gaskets are an essential component of the manufacturing industry, and detecting end-face defects is critical to ensuring their quality. However, current detection methods struggle to detect such defects due to the gaskets’ uneven end-face structure and small defect size. To address such issues, an approach called SR-HorNetYOLO has been proposed. This approach combines traditional machine vision and deep learning techniques. The visual saliency method is used to simplify the image and remove irrelevant features. YOLOv5 is then optimized in three ways: the GhostNet backbone replaces the CSPDarkNet53 backbone to reduce model weight, the self-attention mechanism and bottleneck transformer block improve the precision of detecting small targets, and the feature pyramid is improved through the HorNet network to enhance the predictive performance of the network. Finally, a classification module and position module are employed to classify and locate three-fusion proportion characteristic images. The experimental results show that the SR-HorNetYOLO method achieves a precision of 93.3% and a recall rate of 94.0%.

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A novel laser-based duffing oscillator system to identify weak ultrasonic guided wave signals related to rail defects

Cited by 8 publications

References 19 publications

Evaluation of the Transverse Crack Depth of Rail Bottoms Based on the Ultrasonic Guided Waves of Piezoelectric Sensor Arrays

Evaluation of the Transverse Crack Depth of Rail Bottoms Based on the Ultrasonic Guided Waves of Piezoelectric Sensor Arrays

Rail Flaw Detection via Kolmogorov Entropy of Chaotic Oscillator Based on Ultrasonic Guided Waves

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

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