Abstract
Signal de-noising and feature extraction, whose performance directly affect the evaluation of non-destructive testing (NDT) results, is essential technology for ultrasonic NDT echo processing. Aiming to solve the problem of nonlinear distortion between measured ultrasonic echo and its mathematical model, which widely used are exponential model (EM) and Gauss model (GM), a dual Gaussian attenuation model (DGAM) of ultrasonic echo signal and its parameter estimation method are proposed in this paper. The proposed dual Gaussian attenuation model parameter estimation (DGAM-PE) method is introduced in three parts: calculating mean square error between measured signal and model, optimizing mean square error by particle swarm optimization, optimum parameters extraction based on optimization result. The simulation and experiment results show that compared with the exponential model and Gaussian model, the proposed dual gaussian attenuation mathematical model of ultrasonic signal in this paper can better simulate the measured ultrasonic echo signal, with a mean square error of 0.0073 and normalized correlation coefficient of 0.9816. Additionally, an improved adaptive particle swarm optimization is proposed in order to enhance the accuracy of parameter estimation results.
Highlights
Most materials can be penetrated by ultrasound, which is usually used to detect the internal and surface defects of these materials, and evaluate their physical and mechanical properties
In order to verify that the proposed method can effectively process the measured ultrasonic signal, the ultrasonic echoes of tensile fatigue test of metal components by transmission method are collected, using RITEC RAM-5000-SNAP ultrasonic testing system produced by RITEC Company, and processed in the laboratory
To verify the validity of the proposed ultrasonic echo model, the dual Gaussian attenuation model (DGAM), exponential model (EM) and Gaussian model (GM) are matched with the measured ultrasonic scitation.org/journal/adv
Summary
Most materials can be penetrated by ultrasound, which is usually used to detect the internal and surface defects of these materials, and evaluate their physical and mechanical properties. Signal de-noising and feature extraction, whose performance directly affect the evaluation of non-destructive testing results, is crucially important technology for ultrasonic non-destructive testing data processing, and is essential in ultrasonic NDT technology.3,4 It has important academic meaning and practical value that accurate parameters estimation of an ultrasonic signal in ultrasonic NDT.. Ai-Ling Qi et al., proposes an artificial bee colony optimization based matching pursuit approach (ABC-MP) for ultrasonic signal decomposition by integrating the artificial bee colony algorithm into the matching pursuit method in order to improve the velocity of parameter estimation. All these methods involve the ultrasonic signal model, which is directly related to the accuracy of the parameter estimation.
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