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Sparse representation method for extracting weld characteristic guided wave defect echo signal characteristics

A technology of sparse representation and defect echo, applied in the field of defect detection, can solve the problems of non-stationary guided wave signal and difficult to accurately extract defect features, and achieve the effect of accurate positioning

Active Publication Date: 2019-06-14
JIANGSU UNIV
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Problems solved by technology

However, the characteristic guided wave signal of the weld is always a non-stationary signal, which makes it difficult to accurately extract the defect features only by frequency domain analysis

Method used

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  • Sparse representation method for extracting weld characteristic guided wave defect echo signal characteristics
  • Sparse representation method for extracting weld characteristic guided wave defect echo signal characteristics
  • Sparse representation method for extracting weld characteristic guided wave defect echo signal characteristics

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Experimental program
Comparison scheme
Effect test

Embodiment

[0108] Embodiment: analyze the data that experiment obtains

[0109] During the experiment, as shown in the figure, the signal generator (DG4062) generates a 5-period Hanning window modulation waveform with a center frequency of 200-250kHz to excite the excitation sensor at the top of the weld to form a SH1 mode in the weld, and the signal passes through The propagation of the weld seam to be measured is received by the receiving sensor, amplified by a preamplifier with a gain of 40dB, and sent to a digital oscilloscope (DS2102A) for observation and display.

[0110] Figure 5 (a) shows the echo signal with defects, obviously the defect echo cannot be obtained from Figure 5 identified in (a). The process of applying the method of the present invention to the echo signal is the same as the analysis method of the above-mentioned simulation study. Here we set λ=11, μ=3, and the number of iterations of the split-augmented Lagrangian contraction algorithm is 50. Analysis resul...

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Abstract

The invention discloses a sparse representation method for extracting weld characteristic guided wave defect echo signal characteristics. The sparse representation method for extracting the weld characteristic guided wave defect echo signal characteristics comprises the steps that sparsity of echo signals are used to provide a novel defect feature extraction method; a parameter-adjustable echo signal sparse representation model is established; in the model realization process, an Morlet wavelet atom most similar to a drive signal is adopted to be the basis to construct an overcomplete atomic dictionary, and then a base tracking denoising model is solved through a split augmented Lagrange contraction algorithm; according to wave crest arrival time in obtained reconstructed signals represented by sparse representation, accurate positioning of deflect positions is achieved; and the effectiveness of the method is verified through simulated and actual weld defect signals.

Description

technical field [0001] The invention relates to the field of signal analysis and processing, which belongs to non-destructive testing, and in particular to a detection method for sparse representation of each component in a signal under strong background noise and a defect detection method, which can be used for guided wave return of medium-thick plates and large pressure vessels, etc. Wave signal feature extraction and defect detection. Background technique [0002] Ultrasonic guided wave testing technology has been widely used in the field of nondestructive testing. The characteristic guided wave technology is applied to the defect detection of the weld. When a local defect occurs in the weld, the defect echo packet will be returned when the guided wave encounters a defect. The wave packet contains the echo signals of each mode, which carries the structural information and defect information of the weld. However, the defect echo signal is usually seriously affected by th...

Claims

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Application Information

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IPC IPC(8): G01N29/44
Inventor 许桢英万东燕樊薇张孝龙杨卿吴梦琪王元霞
Owner JIANGSU UNIV
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