This paper presents a method based on signal correlation to detect delamination defects of widely used carbon fiber reinforced plastic with high precision and a convenient process. The objective of it consists in distinguishing defect and non-defect signals and presenting the depth and size of defects by image. A necessary reference signal is generated from the non-defect area by using autocorrelation theory firstly. Through the correlation calculation results, the defect signal and non-defect signal are distinguished by using Euclidean distance. In order to get more accurate time-of-flight, cubic spline interpolation is introduced. In practical automatic ultrasonic A-scan signal processing, signal correlation provide a new way to avoid problems such as signal peak tracking and complex gate setting. Finally, the detection results of a carbon fiber laminate with artificial delamination through ultrasonic phased array C-scan acquired from Olympus OmniScan MX2 and this proposed algorithm are compared, which showing that this proposed algorithm performs well in defect shape presentation and location calculation. The experiment shows that the defect size error is less than 4%, the depth error less than 3%. Compared with ultrasonic C-scan method, this proposed method needs less inspector’s prior-knowledge, which can lead to advantages in automatic ultrasonic testing.

https://www.mdpi.com/1996-1944/13/17/3840/pdf

High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation Algorithm.

Pipeline inspection gauges (PIGs) carry out automatic pipeline inspection with nondestructive testing (NDT) technologies like ultrasound, magnetic flux leakage, and eddy current. The ultrasonic straight beam allows technicians to determine the wall thickness of the pipeline through the time of flight diffraction (TOFD), providing the pipeline reconstruction and allowing the detection of several defects like dents or corrosion. If the pipeline is of a long distance, then the inspection process is automatic, and the fluid pressure pushes the PIG through the pipeline system. In this case, the PIG velocity and its axial alignment with the pipeline cannot be controlled. The PIG geometry, the pipeline deformations, and the girth welds cause a continuous chattering when the PIG is running, removing the transducers perpendicularity with the inspection points, which means that some echoes cannot be received. To reduce this problem, we propose a novel method to design a sensor carrier that takes into account the angularity and distance effects to acquire the straight beam echoes. The main advantage of our sensor carrier is that it can be used in concave and convex pipeline sections through geometric adjustments, which ensure that it is in contact with the inner pipe wall. Our improvement of the method is the characterization of the misalignment between the internal wall of the pipeline and the transducer. Later, we analyzed the conditions of the automatic pipeline inspection, the existing recommendations in state-of-the-art technology, and the different mechanical scenarios that may occur. For the mechanical design, we developed all the equations and rules. At the signal processing level, we set a fixed gain in the filtering step to obtain the echoes in a defined distance range without saturating the acquisition channels. For the validation, we compared through the mean squared error (MSE) our sensor carrier in a straight pipe section and a pipe elbow of steel versus other sensor carrier configurations. Finally, we present the design parameters for the development of the sensor carrier for different pipeline diameters.

https://www.mdpi.com/1424-8220/19/6/1394/pdf

Design and Validation of an Articulated Sensor Carrier to Improve the Automatic Pipeline Inspection

Received: 5 January 2019 Accepted: 12 March 2019 To remove the noise in the echo signals of ultrasonic pulse-echo testing, this paper puts forward a denoising algorithm based on the generalized S transform (GST) and singular value decomposition (SVD). Firstly, the ultrasonic echo signals were subjected to the GST, yielding the time-frequency matrix of the signals. Next, the matrix was taken as the Hankel matrix, and went through the SVD. The threshold for singular values to be zeroed was determined by the ratio between singular entropy increments. After zeroing the singular values representing the noise, the resulting denoised 2D time-frequency matrix was subjected to the inverse GST, generating the denoised echo signals. Our method was applied to denoise the simulated ultrasonic echo signals with different signal-to-noise ratios (SNRs), and compared with the wavelet soft thresholding (WST) method. The comparison show that our method outperformed the WST, especially in denoising the signals with low SNR. In addition, a scanning acoustic microscopy (SAM) system was designed for the experimental verification of our method. The C-scan image with our method was much better than that without our method. Hence, our method was proved feasible and effective.

/pdf/denoising-ultrasonic-echo-signals-with-generalized-s-ap8lz3dnsx.pdf

Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition

Monitoring pipeline wall is an important issue in oil and gas industries. Over time, the defect can occur in the pipeline and can impact surrounding population, environment and may result in injuries or fatalities. While flaws in the pipeline could be detected by ultrasonic testing and monitoring the severity of the flaw. The limitation of ultrasonic testing is the signal contaminate with backscattering noise, which masks flaw echoes in the measured signal. Signal processing take place in the recent year to de-noising for improving signal-to-noise ratio and extract the feature for flaws classification. This paper presents a comprehensive overview of signal processing techniques used to improve ultrasonic detection method with and without intelligent classifier. Finally, the advantages and disadvantages feature extraction provided for classifications process.

Ultrasonic signal processing techniques for Pipeline: A review

In nondestructive testing (NDT), geometrical features of a flaw embedded in the material such as its location, length, and orientation are critical factors to assess the severity of the flaw and make post-manufacturing decisions to improve the design. In this study, artificial intelligence (AI) based NDT approach was applied to the ultrasonic oscillograms obtained from virtual ultrasonic NDT to estimate geometrical features of a flaw. First, a numerical model of NDT specimen was constructed using acoustic finite element analysis (FEA) to produce the ultrasonic signals. The model was validated by comparing the simulated signals produced from the numerical model with the experimental data from actual NDT tests. Then, 750 numerical models containing flaws with different locations, lengths, and orientation angles were generated by FEA. Next, the oscillograms produced by the models were divided into 3 datasets: 525 for training, 113 for validation, and 112 for testing. Training inputs of the network were parameters extracted from ultrasonic signals by fitting them to sine functions. Lastly, to evaluate the network performance, outputs of the network including flaw’s location, length, and angle were compared with the desired values for all datasets. Deviations of the outputs from desired values were calculated by a regression analysis. Statistical analysis was also performed by measuring root mean square error (RMSE) and efficiency. RMSE in x-location, y-location, length, and angle estimations are 0.09 mm, 0.19 mm, 0.46 mm, and 0.75°, with efficiencies of 0.9229, 0.9466, 0.9140, and 0.9154, respectively for the testing dataset. Results suggest that the proposed AI-based method has the potential to interpret the oscillograms from ultrasonic NDT to estimate geometrical features of flaws embedded in the material.

Assessment of Geometrical Features of Internal Flaws with Artificial Neural Network

When detecting the ultrasonic flaw of thick-walled pipe, the flaw echo signals are often interrupted by scanning system frequency and background noise. In particular when the thick-walled pipe defect is small, echo signal amplitude is often drowned in noise signal and affects the extraction of defect signal and the position determination accuracy. This paper presents the modified S-transform domain singular value decomposition method for the analysis of ultrasonic flaw echo signals. By changing the scale rule of Gaussian window functions with S-transform to improve the time-frequency resolution. And the paper tries to decompose the singular value decomposition of time-frequency matrix after the S-transform to determine the singular entropy of effective echo signal and realize the adaptive filter. Experiments show that, using this method can not only remove high frequency noise but also remove the low frequency noise and improve the signal-to-noise ratio of echo signal.

https://downloads.hindawi.com/journals/mpe/2015/312620.pdf

Shiyuan Zhou

Papers

Study on the Thick-Walled Pipe Ultrasonic Signal Enhancement of Modified S-Transform and Singular Value Decomposition