Minimum Entropy Deconvolution을 이용한 회전체 시스템의 결함 모니터링

The invention relates to a frequency domain sparse inversion-based quantitative detection method for defects in a TOFD (time of flight diffraction) blind area and belongs to the field of non-destructive testing technologies. According to the method, a test system composed of a TOFD ultrasonic detector, a TOFD probe, a wedge and a scanning device is adopted; a near surface area in TOFD detection isscanned, and collected aliased time-domain signals are processed, so that a sparse inversion model is established; with the sparseness and decomposable characteristics of a reflection sequence considered, an objective function for TOFD blind area detection is established in a frequency domain; high-signal-to-noise ratio portion spectral data are selected, and inversion is performed on the high-signal-to-noise ratio portion spectral data, and the separation of aliasing signals comprising straight-through waves, defect upper end point diffraction waves and defect lower end point diffraction waves is realized; and acoustic-path difference between the straight-through waves, the defect upper end point diffraction waves and the defect lower end point diffraction waves is read directly, the buried depth and height of defects in the near-surface blind area are determined. Compared with other near-surface defect detection methods, the method can separate a plurality of aliasing signals, can simultaneously measure the depth and height of the defects in the near-surface blind area, has no additional requirements for a hardware system, and has a high engineering application value.

Frequency domain sparse inversion-based quantitative detection method for defects in TOFD (time of flight diffraction) blind area

In this paper we present a blind deconvolution scheme based on statistical wavelet estimation. We assume no prior knowledge of the wavelet, and do not select a reflector from the signal. Instead, the wavelet (ultrasound pulse) is statistically estimated from the signal itself by a kurtosis-based metric. This wavelet is then used to deconvolve the RF (radiofrequency) signal through Wiener filtering, and the resultant zero phase trace is subjected to spectral broadening by Autoregressive Spectral Extrapolation (ASE). These steps increase the time resolution of diffraction techniques. Results on synthetic and real cases show the robustness of the proposed method.

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Zhaorui Liu

Papers

Improving Time Estimation by Blind Deconvolution: with Applications to TOFD and Backscatter Sizing.