基于CEEMDAN-SampEn-改进小波阈值的重介质旋流器振动信号处理研究

    Study on Vibration Signal Processing of Dense Medium Cyclone based on CEEMDAN-SampEn-Improved Wavelet Threshold

    • 摘要: 为了有效去除重介质旋流器振动信号中的外部噪声,提升设备故障检测效率和可靠性,解决重介质旋流器振动信号去噪效果差、提取信号特征困难的问题,提出了一种结合自适应噪声完备集合经验模态分解(CEEMDAN)、样本熵(SampEn)和改进小波阈值的信号处理方法。首先使用CEEMDAN抑制模态混叠,生成固有模态函数(IMF);再基于样本熵识别并筛选熵值较高的噪声分量;然后对含噪分量应用改进小波阈值去噪,保留有效信号特征;最后重构去噪后的IMF分量与未受噪声影响的模态分量,得到优化信号。仿真信号的模拟实验表明:该方法在信噪比(SNR)、均方误差(MSE)和波形相似系数(NCC)指标上显著优于传统小波阈值法、CEEMDAN法和CEEMDAN-小波阈值法,其中SNR提升至33.36 dB,MSE降至0.009 5,NCC达0.999 9。实例验证中,对涡北选煤厂重介质旋流器振动信号处理结果显示:样本熵筛选出前6个IMF分量为噪声主导;改进方法能够有效去除高频噪声,同时保留了低频故障特征;其在实际应用中体现出了出色的去噪效果与特征保留能力,Lempel-Ziv复杂度(LZC)回升至47。所提出的CEEMDAN-SampEn-改进小波阈值法通过多尺度分解、熵值筛选与优化阈值去噪,实现了重介质旋流器振动信号的高效去噪,为故障诊断提供了更清晰的信号,该方法在仿真与实例验证中均表现出优异性能,具备重要理论价值与工程应用潜力。

       

      Abstract: To effectively eliminate external noise from the vibration signals of dense medium cyclones and enhance the efficiency and reliability of fault detection, a novel signal processing method combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SampEn), and an improved wavelet threshold technique is proposed. This method addresses the challenges of poor noise reduction and difficulty in feature extraction from dense medium cyclone vibration signals. Firstly, CEEMDAN is employed to suppress mode mixing and generate Intrinsic Mode Functions (IMFs). Subsequently, Sample Entropy is used to identify and filter noise components with higher entropy values. The selected noisy components are then processed using an improved wavelet threshold to remove noise while preserving effective signal features. Finally, the denoised IMF components are reconstructed along with the noise-free modal components to obtain an optimized signal. Simulation experiments demonstrate that the proposed method significantly outperforms traditional wavelet thresholding, CEEMDAN, and the combined CEEMDAN-Wavelet threshold method in terms of Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Normalized Cross-Correlation Coefficient (NCC). Specifically, the SNR increases to 33.36 dB, the MSE decreases to 0.009 5, and the NCC reaches 0.999 9. In a practical validation, the vibration signal processing results from the Dense Medium Cyclone at the Wobei Coal Preparation Plant indicate that the first six IMF components, as identified by Sample Entropy, are predominantly noise. The proposed method effectively removes high-frequency noise while retaining low-frequency fault characteristics, demonstrating superior denoising performance and feature preservation in practical applications. Additionally, the Lempel-Ziv Complexity (LZC) is restored to 47. The proposed CEEM-DAN-SampEn-Improved Wavelet Threshold method achieves efficient noise reduction of dense medium cyclone vibration signals through multi-scale decomposition, entropy-based filtering, and optimized threshold denoising. It provides clearer signals for fault diagnosis and has demonstrated excellent performance in both simulation and practical validation, highlighting its significant theoretical value and engineering application potential.

       

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