Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

Gongzhang, Rui; Li, Minghui; Xiao, Bo; Lardner, Timothy; Gachagan, Anthony

doi:10.1063/1.4865062

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Robust defect detection in the presence of grain noise is a challenging yet essential problem in ultrasonic non-destructive evaluation (NDE), which has attracted significant attention in recent decades [1][2][3][4][5][6][7][8][9][10]. A variety of coarse-grained materials such as alloy and austenitic stainless steel offer attractive properties like high-temperature strength or excellent resistance to a corrosive environment and thus are widely used to build components like ducting, combustion cans, and transition liners in a range of key industrial sectors such as energy, transportation, oil and gas, nuclear, and aerospace.…”

Section: Introductionmentioning

confidence: 99%

“…The defect and grain boundaries usually have different sizes, thus their scattering properties and responses to the broadband excitation signal demonstrate different spectral characteristics [2]. These differences have motivated temporal-spectral processing, such as Weiner filtering [3,4], split-spectrum processing [5], sub-spectrum phase coherence factoring [6], spectral distribution similarity analysis [7], and matched filtering [8]. On the other hand, the defect echo typically has a coherent structure with energy mainly scattered from a single spatial point, while the grain noise is spatially distributed throughout the insonified resolution cell.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Ultrasonic Imaging and Non-Destructive Evaluation Using Angle-of-Arrival Estimation in Dimension-Reduced Beam Space

2020

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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Abstract Robust defect detection in the presence of grain noise originating from material microstructures is a challenging yet essential problem in ultrasonic non-destructive evaluation (NDE). In this paper, a novel method is proposed to suppress the gain noise and enhance the defect detection and imaging. The defect echo and grain noise are distinguished through analyzing the spatial location where the echo is originating from. This is achieved by estimation of the angle of arrival (AOA) of the returned echo and evaluation of the likelihood that the echo is reflected from the point where the array is focused or otherwise from the random reflectors like the grain boundaries. The method explicitly addresses the statistical models of the defect echoes and the spatial noise across the array aperture, as well as the correlation between the flaw signal and the interfering echoes; estimates the AOA and the likelihood in a dimension-reduced beam space via a linear transformation; and determines a weighting factor based on the mean likelihood. The factors are then normalized and utilized to correct and weigh the NDE images. Experiments on industrial samples of austenitic stainless steel and INCONEL Alloy 617 are conducted with a 5 MHz transducer array, and the results demonstrate that the grain noise is reduced by about 20 dB while the defect reflection is well retained, thus the great benefits of the method on enhanced defect detection and imaging in ultrasonic NDE are validated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Ultrasonic Imaging and Non-Destructive Evaluation Using Angle-of-Arrival Estimation in Dimension-Reduced Beam Space

2020

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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“…An advanced algorithm, Moving Bandwidth Polarity Thresholding (MBPT) [9], has been developed to solve the sensitivity problems of SSP. However, MBPT also cannot fully remove such artefacts and hence, cannot maintain a high defect detectability.…”

Section: Introductionmentioning

confidence: 99%

Advanced defect detection algorithm using clustering in ultrasonic NDE

Gongzhang¹,

Gachagan²

2016

AIP Conference Proceedings

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A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the image interpretation stage. This paper presents an advanced algorithm to further reduce the influence of artefacts remaining in A-scan data after processing using a conventional defect detection algorithm. The raw A-scan data can be acquired from either traditional single transducer or phased array configurations. The proposed algorithm uses the concept of unsupervised machine learning to cluster segmental defect signals from pre-processed Ascans into different classes. The distinction and similarity between each class and the ensemble of randomly selected noise segments can be observed by applying a classification algorithm. Each class will then be labelled as 'legitimate reflector' or 'artefacts' based on this observation and the expected probability of defection (PoD) and probability of false alarm (PFA) determined. To facilitate data collection and validate the proposed algorithm, a 5MHz linear array transducer is used to collect A-scans from both austenitic steel and Inconel samples. Each pulse-echo A-scan is pre-processed using SSP and the subsequent application of the proposed clustering algorithm has provided an additional reduction to PFA while maintaining PoD for both samples compared with SSP results alone

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“…Hence, a robust frequency diversity based algorithm, Moving Bandwidth Polarity Thresholding (MBPT), was recently devised by the authors [5]. By exploring the phase coincidence within a moving bandwidth across the signal spectrum, MBPT reduces the effect of parameter sensitivity problems while maintaining high performance.…”

Section: Introductionmentioning

confidence: 99%

Clutter noise reduction for phased array imaging using frequency-spatial polarity coherence

Gongzhang¹,

Gachagan²,

Xiao³

2015

AIP Conference Proceedings

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A number of materials used in industry exhibit highly-scattering properties which can reduce the performance of conventional ultrasonic NDE approaches. Moving Bandwidth Polarity Thresholding (MBPT) is a robust frequency diversity based algorithm for scatter noise reduction in single A-scan waveforms, using sign coherence across a range of frequency bands to reduce grain noise and improve Signal to Noise Ratio. Importantly, for this approach to be extended to array applications, spatial variation of noise characteristics must also be considered. This paper presents a new spatial-frequency diversity based algorithm for array imaging, extended from MBPT. Each A-scan in the full matrix capture array dataset is partitioned into a serial of overlapped frequency bands and then undergoes polarity thresholding to generate sign-only coefficients indicating possible flaw locations within each selected band. These coefficients are synthesized to form a coefficient matrix using a delay and sum approach in each frequency band. Matrices produced across the frequency bands are then summed to generate a weighting matrix, which can be applied on any conventional image. A 5MHz linear array has been used to acquire data from both austenitic steel and high nickel alloy (HNA) samples to validate the proposed algorithm. Background noise is significantly suppressed for both samples after applying this approach. Importantly, three side drilled holes and the back wall of the HNA sample are clearly enhanced in the processed image, with a mean 133% Contrast to Noise Ratio improvement when compared to a conventional TFM image

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Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

Cited by 6 publications

References 11 publications

Enhanced Ultrasonic Imaging and Non-Destructive Evaluation Using Angle-of-Arrival Estimation in Dimension-Reduced Beam Space

Enhanced Ultrasonic Imaging and Non-Destructive Evaluation Using Angle-of-Arrival Estimation in Dimension-Reduced Beam Space

Advanced defect detection algorithm using clustering in ultrasonic NDE

Clutter noise reduction for phased array imaging using frequency-spatial polarity coherence

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