Investigating electromagnetic acoustic emission signals denoising for alloy materials non-destructive detecting: a CRQA method

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Аннотация

Aiming at the problem that signals collected from local electromagnetic loading operations are usually mixed with background noises (especially white noise), this paper proposed an electromagnetic acoustic emission signal denoising technology based on cross recurrence quantification analysis (CRQA). Firstly, the decomposition layer and penalty factor of variational mode decomposition (VMD) are set by experience or optimization algorithm, and then the original signal is decomposed. Secondly, the main components are selected by the CRQA algorithm, and the electromagnetic acoustic emission signal after denoising is obtained by superposition reconstruction. The simulation and experimental results show that when 5dB noise is added, CRQA can effectively remove the background noises in electromagnetic acoustic emission signals compared to the correlation coefficient algorithm, and it can assist in realizing the high-precision non-destructive testing of alloy materials.

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Авторлар туралы

Qiuyue Li

Chongqing Vocational Institute of Safety Technology, Department of Network and Information Security; Chongqing Three Gorges University, School of Mechanical Engineering

Email: Laiys@Sanxiau.edu.cn
ҚХР, Chongqing; Chongqing

Yushu Lai

Chongqing Three Gorges University, School of Mechanical Engineering

Хат алмасуға жауапты Автор.
Email: Laiys@Sanxiau.edu.cn
ҚХР, Chongqing

Difei Cao

University of Science and Technology Beijing, School of Computer & Communication Engineering

Email: Laiys@Sanxiau.edu.cn
ҚХР, Beijing

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2. Fig. 1. Time and frequency domains of the modeled signal with noise

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3. Fig. 2. Schematic diagram of the EMAE experiment

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4. Fig. 3. Experimental platform

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5. Fig. 4. Sample used in the experiment

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6. Fig. 5. Amplitude of the signal obtained on the 6061 aluminum alloy sample in time and frequency domains

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7. Fig. 6. Signal amplitude obtained on the 6061 aluminum alloy sample in time and frequency domains with Gaussian white noise

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8. Fig. 7. Amplitude of the signal obtained on the 6061 aluminum alloy specimen in time and frequency domains after GAVD-CRQA treatment

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9. Fig. 8. Signal amplitude obtained on AZ31B magnesium alloy sample in time and frequency domains with Gaussian white noise

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10. Fig. 9. Amplitude of the signal obtained on the AZ31B magnesium alloy specimen in time and frequency domains after treatment by GAVD-CRQA method

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