Abstract
Microcharge induction has recently been applied as a dust detection method. However, in complex environments, the detection device can be seriously polluted by noise. To improve the quality of the measured signal, the characteristics of both the signal and the noise should be analyzed so as to determine an effective noise removal method. Traditional removal methods mostly deal with specific noise signals, and it is difficult to consider the correlation of measured signals between adjacent time periods. To overcome this shortcoming, we describe a method in which wavelet decomposition is applied to the measured signal to obtain sub-band components in different frequency ranges. A time-lapse Pearson method is then used to analyze the correlation of the sub-band components and the noise signal. This allows the sub-band component of the measurement signal that has the strongest correlation with the noise to be determined. Based on multifractal detrended fluctuation analysis combined with empirical mode decomposition, the similarity between the signal sub-band components and the noise sub-band components is analyzed and three indices are employed to determine the multifractal characteristics of the sub-band components. The consistency between noise components and signal components is obtained and the main signal components are verified. Finally, the sub-band components are used to reconstruct the signal, giving the noise-free measured (microcharge induction) signal. The filtered signal presents smoother, multifractal features.
Highlights
One of the notable properties of dust is its electrification during movement. ere has been considerable research on the chargeability of dust and how to measure the electrostatic signal [1,2,3,4,5,6], and it has been widely used in the detection of gas flow velocity in the field of pneumatic conveying
Xu et al [12, 13] proposed a new type of electrostatic sensor system for measuring the pulverized coal speed and relative mass flow, which determines the speed of pulverized coal from the autocorrelation of the output signal of the electrostatic sensor array. e relative concentration and mass flow rate are obtained from the root mean square value output by two ring electrodes. e present authors [6] have conducted research on the theory of mine dust belt motors, charging modes, charging measurement models, and measuring devices and have identified nonlinear characteristics in the dust electrostatic signal that reflect the multiscale information contained in the signal
multifractal detrended fluctuation analysis (MFDFA) can effectively describe the nonlinear measurement signal, especially the multifractal characteristics of the time series, but the analysis of the time series signal requires a detrending process, which causes pseudofluctuation errors to appear. ere are two main reasons for this: one is that the sequence is over- or underfitted due to the uncertainty of the order of the fitted polynomial function; the other is that MFDFA uses a uniform sequence for data segmentation, resulting in sequence segmentation points that are not continuous
Summary
One of the notable properties of dust is its electrification during movement. ere has been considerable research on the chargeability of dust and how to measure the electrostatic signal [1,2,3,4,5,6], and it has been widely used in the detection of gas flow velocity in the field of pneumatic conveying. E multilayer wavelet decomposition transform is an effective signal-noise separation method in both the time domain and frequency domain. Ere are two main reasons for this: one is that the sequence is over- or underfitted due to the uncertainty of the order of the fitted polynomial function; the other is that MFDFA uses a uniform sequence for data segmentation, resulting in sequence segmentation points that are not continuous To solve these problems, Li et al [34] proposed an MFDFA algorithm based on empirical mode decomposition (EMD) and template movement. The EMD-MFDFA algorithm is used to analyze the similarity between the signal sub-band and the noise sub-band and determine the wavelet sub-bands in which the electrostatic signal is located, and the results show that a4, a5, d4, and d5 are the signal components. The noise is filtered and the denoised electrostatic signal is reconstructed with a4, a5, d4, and d5
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