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Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

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Noise reduction method for mine wind speed sensor data based on CEEMDAN-wavelet threshold.

Yu Wang^1,2, Jian Liu^3,4, Dong Wang^1,5

¹College of Safety Science and Engineering, Liaoning Technical University, Huludao, 125105, Liaoning, China.

Scientific Reports

|October 22, 2024

Summary

This study introduces a novel denoising method for mine wind speed sensors, combining adaptive complete ensemble empirical mode decomposition (CEEMDAN) with wavelet thresholds. The method effectively suppresses turbulent pulsation noise, improving wind speed measurement accuracy in intelligent ventilation systems.

Keywords:

CEEMDAN Data noise reduction Mine intelligent ventilation Mine wind speed sensor Wavelet threshold

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Area of Science:

Mining Engineering
Signal Processing
Sensor Technology

Background:

Mine wind speed sensors are crucial for intelligent ventilation systems.
Turbulent airflow pulsations cause inaccurate wind speed measurements in underground tunnels.
Existing denoising methods may not fully address the non-stationary nature of wind speed signals.

Purpose of the Study:

To develop an advanced denoising technique for mine wind speed signals.
To improve the accuracy of wind speed measurements in intelligent mining environments.
To address the challenges posed by turbulent pulsation noise.

Main Methods:

Adaptive Complete Ensemble Empirical Mode Decomposition (CEEMDAN) for signal decomposition.
Continuous mean square error criterion to identify noisy components.
Wavelet threshold denoising applied to high-frequency intrinsic mode functions (IMFs).
Reconstruction of denoised signal from processed and low-frequency IMFs.

Main Results:

The proposed CEEMDAN-wavelet threshold method significantly reduces noise in wind speed signals.
The denoising technique achieves a higher signal-to-noise ratio (SNR) and lower root mean square error (RMSE).
Comparative analysis shows superior performance over traditional EMD-wavelet and EEMD-wavelet methods.

Conclusions:

The novel joint denoising method offers enhanced accuracy for mine wind speed sensor data.
This approach provides a reliable new technique for processing turbulent pulsation signals in mining.
The findings contribute to the development of more robust intelligent mining ventilation systems.