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工业控制系统常发生的故障是执行机构和检测装置的故障 ,且故障信号多是突变性的。传统的Fourier分析由于在时域缺乏空间局部性 ,只能确定一个函数奇异性的整体性质 ,而难以确定奇异点在空间的位置及分布情况 ,难以检测到突变信号。小波变换具有空间局部化性质 ,而且时域窗和频域窗的宽度可调节。对系统的输入、输出信号进行小波变换 ,利用该变换求出输入输出信号的奇异点。仿真实验证明了小波变换在故障检测中所具有的优越性
Industrial control systems often occur in the failure of the actuator and the detection device failure, and fault signals are mostly mutable. Because of the lack of spatial locality in the time domain, the traditional Fourier analysis can only determine the global property of a singularity of the function, and it is difficult to determine the position and distribution of the singularities in space, making it difficult to detect the mutation signal. The wavelet transform has the property of spatial localization, and the width of time-domain window and frequency domain window can be adjusted. The input and output signals of the system are transformed by wavelet transform, and the singular points of the input and output signals are obtained by this transformation. The simulation experiment proves the superiority of wavelet transform in fault detection