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EMAT noise suppression using information fusion in stationary wavelet packets
Michal Kubinyi1, Ondrej Kreibich, Jan Neuzil
1Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Measurement, Prague, Czech Republic. michal.kubinyi@fel.cvut.cz
Summary
This study introduces a novel ultrasonic signal filtering method for electromagnetic acoustic transducer (EMAT) pulse-echo signals. The new stationary wavelet packet denoising technique significantly enhances signal-to-noise ratio (SNR) in nondestructive testing.
Area of Science:
- Nondestructive Testing
- Signal Processing
- Acoustic Engineering
Background:
- Ultrasonic nondestructive testing (NDT) faces challenges in detecting flaw echoes amidst background noise from instrumentation and clutter.
- Existing noise filtering methods like signal averaging and wavelet transforms are not optimized for the impulse nature of electromagnetic acoustic transducer (EMAT) signals.
Purpose of the Study:
- To develop an advanced ultrasonic signal filtering approach tailored for EMAT pulse-echo signals.
- To improve the detection of flaw echoes in noisy NDT environments.
Main Methods:
- Proposed a stationary wavelet packet denoising method.
- Incorporated a threshold influenced by statistical echo detection, wavelet coefficient amplitude distribution, and known system frequency characteristics.
- Evaluated the method on EMAT signals under various signal-to-noise ratio (SNR) conditions.
Main Results:
- The proposed method demonstrated superior performance compared to wavelet transform with Stein unbiased risk estimate (SURE) and split-spectrum processing (SSP).
- Achieved a significant SNR enhancement of 19 dB with real EMAT data.
- Effectively filters noise in ultrasonic signals for improved flaw detection.
Conclusions:
- The novel stationary wavelet packet denoising method is highly effective for filtering ultrasonic signals from EMATs.
- This approach offers a significant improvement in SNR, crucial for accurate flaw detection in NDT.
- The method's adaptive thresholding strategy enhances its robustness across different noise conditions.
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