小波变换用于在线数据能提高去噪性能.针对传统小波在线使用时运算慢的问题,将基于提升格式的小波用于在线滤波.相比传统小波,基于提升格式的小波运算更快,能更好地满足实时性的要求.为了降低小波分析中边界效应的影响,提出一种新的简单数据扩展方法,该方法用先前时刻的滤波输出和噪声信息预测边界点后的数据信息.仿真结果表明:新方法运算速度快,满足实时处理的要求,能降低信号均方误差值并改善伪吉布斯效应.工业现场的在线滤波实例证明了该方法的可行性和良好的性能.%Applying wavelet transform to online data filtering can improve the denoising performance. Considering slow data processing of the traditional wavelet, a simple online data filtering method based on lifting wavelet was proposed, and compared to the traditional wavelet shows that the lifting wavelet can improve data processing speed significantly; and moreover, a simple boundary extension method was presented so that the boundary influence on wavelet transform can be decreased, this method uses the output data and noise information in previous time to predict the data information of boundary. Simulation studies demonstrate that this new method can decrease the signal' s mean-square error and the negative effect from the pseudo-Gibbs. Industrial application proves its applicability and good performance.
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