【摘要】 烟气发电机组是石油化工行业中的一种高速旋转大型机械,用于将催化裂化再生烟气中具有的能量转变成机械能。为保证机组安全、稳定运行而进行的运行状态监测,需解决机组在大量背景噪声下状态特征弱信息提取的问题。为此研究基于Birgé-Massart惩罚函数策略获取阈值的信号小波变换模极大值抑制噪声算法。由Birgé-Massart惩罚函数策略获取阈值,在小波变换不同分解尺度上构建不同的模极大值点邻域来影响模极大值点搜索过程,获得小波分解各尺度上合适的模极大值点序列,凸显状态特征信息,利用Mallat的交错投影法重构信号。为验证该算法的有效性,将该算法与Rigrsure、Sqtwolog、Heursure、Minimaxi四种阈值抑噪方法进行比较,结果表明该算法具有更好的信噪比和方均根误差。应用该算法对现场采集的振动信号进行状态特征弱信息提取处理,结果表明处理后的信号在保留突变点的同时,具有良好的光滑性,可以用来进行烟气发电机组状态特征信息的提取。更多还原

【Abstract】 Flue gas generator set is a kind of large high-speed rotating machinery in petrochemical industry,which function is transfer the energy in catalytic cracking regeneration flue gas into mechanical energy.The problem of extracting state feature weak information under large amount of background noise need to be resolved in order to ensure the flue gas generator set operation safe and stable.To this end,noise suppression algorithms on the basis of Birgé-Massart penalty function strategy is researched to obtain signal wavelet transform modulus maximum and threshold.The threshold is obtained through penalization strategy provided by Birge-Massart,then,different modulus maximum vertex neighborhood are constructed in different wavelet transform decomposition scales which influence the search process of modulus maximum point,so,the appropriate modulus maximum points sequence are obtained on various wavelet decomposition scales.Finally,signals are reconstruct using Mallat staggered projection.In order to validate the effectiveness of the algorithm,it was compared with four kinds of threshold noise suppression methods namely Rigrsure,Sqtwolog,Heursure,Minimaxi,and the results show that this algorithm has a better signal to noise ratio and mean-square error.The algorithm is applied in to extracting state feature weak information of vibration signals collected on the industrial scene,the results show that the signals after processed has good smoothness while retaining the mutation point and can be used for state feature information extraction of exhaust gas generator set.更多还原