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针对非线性、非平稳信号的数据压缩问题,提出了一种基于自适应形态小波的轧机电气信号压缩方法.结合电气信号的形态特征,采用中值算子作为形态小波的更新算子对信号进行分解,从而实现根据信号的局部形态特征,自适应地调整形态小波分解的更新算子.工业现场实际轧机电气信号的数据压缩实验证明:利用这种形态小波信号压缩方法,可以获得高压缩比的信号,并能保留信号的形态特征;同时,这种形态小波信号压缩方法运算量小,可以应用到实时性要求较高的在线监测系统中.
Aiming at the problem of data compression of nonlinear and non-stationary signals, a rolling mill electrical signal compression method based on adaptive morphological wavelet is proposed. Combining with the morphological characteristics of electrical signals, a median operator is used as an update operator of morphological wavelet to signal So as to adaptively adjust the update operator of the morphological wavelet decomposition according to the local morphological characteristics of the signal.Experimental data compression experiments of the actual rolling mill electrical signal at the industrial site show that using this morphological wavelet signal compression method can obtain high compression ratio Signal, and can retain the morphological characteristics of the signal; the same time, this morphological wavelet signal compression method is small, and can be applied to on-line monitoring system with high real-time requirements.