基于FPGA的超声信号自适应滤波与特征提取

doi:10.19595/j.cnki.1000-6753.tces.190572

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Abstract Aiming at the nonlinear and non-stationary characteristics of electromagnetic ultrasonic characteristic signals, there are problems that traditional noise reduction components and features are difficult to extract. A data processing algorithm for adaptive filtering of electromagnetic ultrasonic signals and the empirical mode decomposition (EMD) method is proposed. Firstly, the stability evaluation of the ultrasonic signal is carried out. On this basis, the adaptive ultrasonic filtering is used to denoise the electromagnetic ultrasonic signal. The adaptive filtering integrated into the EMD is more sensitive to the unique frequency noise. The EMD is used to decompose the fluctuating time and frequency at different time scales. The information and the noise frequency components involved are used to realize the feature extraction. The reconstruction of the ultrasonic signal after EMD denoising can eliminate the frequency aliasing phenomenon, and realize the real-time noise reduction and feature extraction of the electromagnetic ultrasonic signal based on FPGA. The basis for further defect identification and defect assessment and portability has been laid. Finally, the experimental study on aluminum plates with microcracks and plastic damage was carried out, and the effectiveness of the method was verified. The method has the characteristics of high signal-to-noise ratio, real-time extraction of time-frequency information and less loss of effective information, and can effectively identify defects in the aluminum plate.

Key words： Ultrasonic characteristic signals adaptive filtering empirical mode decomposition feature extraction FPGA

Received: 14 May 2019

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TB552

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	Liu Suzhen
	Wei Jian
	Zhang Chuang
	Jin Liang
	Yang Qingxin

Cite this article:

Liu Suzhen,Wei Jian,Zhang Chuang等. Adaptive Filtering and Feature Extraction of Ultrasonic Signal Based on FPGA[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2870-2878.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190572 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I13/2870

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