Damage quantification of aluminum plates using SC-DTW method based on Lamb waves

Wei, Daiping; Liu, Xiaofeng; Wang, Bangxin; Tang, Zhi; Lin, Bo

doi:10.1088/1361-6501/ac4435

Cited by 7 publications

(3 citation statements)

References 29 publications

(32 reference statements)

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“…Based on their work, we might infer that this might be another way to locate damage in carbon fibre reinforced plastic (CFRP). J. Wang et al [115] investigate a nonlinear Lamb wave TR technique for fatigue crack detection and quantification. Using the same technique, which is physical-virtual, TR can be extended to a network of active sensors for fatigue crack localisation and imaging.…”

Section: Wave Propagationmentioning

confidence: 99%

Nonlinear Vibration Analysis of Beam and Plate with Closed Crack: A Review

Tewelde

Krawczuk

2022

Acta Mechanica Et Automatica

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The effect of nonlinearity is high sensitivity in damage detection, especially for closed cracks and delamination. This review illustrates the results of several researchers dealing with nonlinear effects caused by the closure of cracks in the structure, i.e., beam and plate structures. Early detection of damage is an important aspect for the structure and, therefore, continuous progress is being made in developing new and effective methods that use nonlinear effects for early detection of damage and barely visible cracks, i.e., closed cracks and delamination, as well as for the determination of crack size and location. After analysing various methods, the merits, drawbacks and prospects of a number of nonlinear vibration methods for structural damage detection are discussed, and recommendations are made for future researchers.

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Section: Wave Propagationmentioning

confidence: 99%

Nonlinear Vibration Analysis of Beam and Plate with Closed Crack: A Review

Tewelde

Krawczuk

2022

Acta Mechanica Et Automatica

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“…Hence, continuous monitoring of the structural assets with cost-effective damage-sensitive NDT devices and techniques is a priority to avert catastrophic failure, suggest timely maintenance, and save the economy. Most of the previous studies have been on the monitoring and prediction of empty cracks (cracks without debris or dry cracks) in structures using various structural health-monitoring techniques [8][9][10][11][12][13][14]. In recent decades, the guided-wave ultrasonic technique has received more attention than other NDT techniques for SHM [15].…”

Section: Introductionmentioning

confidence: 99%

Confluence Effect of Debris-Filled Damage and Temperature Variations on Guided-Wave Ultrasonic Testing (GWUT)

Olisa,

Khan

2024

Processes

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Continuous monitoring of structural health is essential for the timely detection of damage and avoidance of structural failure. Guided-wave ultrasonic testing (GWUT) assesses structural damages by correlating its sensitive features with the damage parameter of interest. However, few or no studies have been performed on the detection and influence of debris-filled damage on GWUT under environmental conditions. This paper used the pitch–catch technique of GWUT, signal cross-correlation, statistical root mean square (RMS) and root mean square deviation (RMSD) to study the combined influence of varying debris-filled damage percentages and temperatures on damage detection. Through experimental result analysis, a predictive model with an R2 of about 78% and RMSE values of about 7.5×10−5 was established. When validated, the model proved effective, with a comparable relative error of less than 10%.

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“…The light intensity requirements are relatively strict and do not apply to inspect in-service pipelines with internal fluid flow. The methods above and other machine learning methods require manual labeling [28]. It is difficult to effectively extract sufficient training samples in practical pipeline inspection scenarios due to the varying pipeline specifications and the small number of defect samples.…”

Section: Introductionmentioning

confidence: 99%

An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure

Wu,

Huang,

Zhang

et al. 2023

Meas. Sci. Technol.

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Ultrasonic in-line inspection is one of the most widely adopted nondestructive testing methods for defect detection in pipelines. In practical industrial scenes, pipeline specification varies, sensor failure occurs frequently, and the number of pipeline defect samples is scarce. How to detect defects without false detection caused by invalid data and with limited labeled samples is a challenging problem in this area. An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure is proposed in this paper to enhance the accuracy of the defect and its profile detection. Firstly, the multi-channel data is aggregated according to sampling time. The data dimension is reduced, and the accuracy of the invalid waveform detection adopting isolation forest arithmetic is improved. The methods are adopted for the invalid waveforms replacement with two-dimensional cubic spline interpolation using adjacent sensors. Secondly, a natural breakpoint method is adopted to locate the echo peaks. Residual wall thickness is evaluated by calculating the time difference between the echo peaks. A fast pseudo-colorization method based on sliding windows and a morphological image processing method are proposed to detect defects and their profiles efficiently utilizing the residual wall thickness. Finally, practical in-line inspection data is utilized to evaluate the performance. The experiment results illustrate that the detection accuracy is enhanced on different sizes of pipelines without requiring labeled samples.

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Damage quantification of aluminum plates using SC-DTW method based on Lamb waves

Cited by 7 publications

References 29 publications

Nonlinear Vibration Analysis of Beam and Plate with Closed Crack: A Review

Nonlinear Vibration Analysis of Beam and Plate with Closed Crack: A Review

Confluence Effect of Debris-Filled Damage and Temperature Variations on Guided-Wave Ultrasonic Testing (GWUT)

An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure

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