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The paper is focused on finding appropriate procedures of denoising electrical signals (SN) acquired from the secondary winding of the excitation transformer in a large power group and evaluating their benefits. The signals were affected by white Gaussian noise with relatively high Signal to Noise ratios. The currents have significant harmonic contents (total harmonic distortions exceeding 25%). Two...
Special preprocessing techniques for denoising quasi-stationary signals (SN) acquired from the secondary winding of the excitation transformer in a power plant are addressed. Sequences of 30 periods were analyzed. SN were polluted by white Gaussian noise and “average” signals (AS) of one period length were deduced. Because vectors of samples were handled, corrective measures (CM) were applied firstly...
Vibration signal is the main signal used for fault diagnosis of large mechanical and electrical equipment. Because of the bad work condition, the vibration signal contains complex noises and its SNR(signal-to-noise ratio) is very low, so it is difficult to extract the fault feature in vibration signal. According to the characteristic of vibration signal, an denoising method is provided to eliminate...
Time series measured in real world is often nonlinear, even chaotic. To effectively extract desired information from measured time series, it is important to preprocess data to reduce noise. In this Letter, we propose an adaptive denoising algorithm. Using chaotic Lorenz data and calculating root-mean-square-error, Lyapunov exponent, and correlation dimension, we show that our adaptive algorithm more...
This paper describes a low-noise low-offset CMOS readout circuit for MEMS capacitive accelerometers. It employs a feedback capacitance and a combination of switches to have the input parasitic capacitance and the offset voltage canceled. The raised current IDS of the input differential pair in the first stage is used to help reduce sharply the total low-frequency noises without increasing the complexity...
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