基于小波包惩罚函数的烟机振动信号软阈值降噪

Method of Wavelet Packet-Based Penalty Function Soft-Threshold to De-Noise Vibration Signals for Flue Gas Turbine

  • 摘要: 为解决烟机振动信号受到噪声干扰这一问题,研究基于小波包阈值降噪的原理和方法,给出了小波包阈值降噪的步骤,阐述了Birgé-Massart惩罚函数确定阈值的原则和软阈值的量化处理,分析了阈值、信噪比和均方误差随惩罚因子的变化规律. 并将基于小波包惩罚函数的软阈值降噪与Rigrsure、Heursure、Sqtwolog、Minimax 4种阈值降噪方法进行了比较. 结果表明基于惩罚函数的小波包软阈值方法能有效降低噪声. 基于该方法的烟机振动信号降噪在保留信号突变部分的同时,具有良好的光滑性.

     

    Abstract: To solve the problem of vibration signals acquired from flue gas turbine interfered by noise, the principle and method of wavelet packet-based penalty function soft-threshold de-noising are analyzed and its procedure of de-noising is provided. The rule of Birgé-Massart penalty function to determine threshold is clarified and soft-threshold quantization processing is made. Then, the value changes of threshold, SNR and mean square error with the penalty factor are observed and compared with Rigrsure, Heursure, Sqtwolog, Minimax soft-threshold de-noising methods. The results show that the penalty function soft-threshold method can effectively reduce the noise. The de-noised vibration signals of flue gas turbine based on this method can retain mutant part of the signal, while it still has good smoothness.

     

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