Abstract

The suspended dust produced in the production of process industry may cause damage to occupational health and even trigger explosion accidents. To accurately detect the dust concentration, and to reduce the noises and standard deviation volatility of the inductive signal of characterized dust concentration generated when applying the electrostatic induction method, the study designs the Kalman filter based on a machine learning algorithm and realizes secondary processing of the standard deviation of inductive signal. Firstly, this study designs the dust concentration detection device using the electrostatic induction method, realizing an effective extraction and amplification of the dust inductive signal, confirming a positive correlation between the volatility of the inductive signal and dust concentration. A data processing procedure for the inductive signal is also designed. To eliminate the standard deviation volatility of inductive signal, the Kalman filter aided by machine learning is selected to process mathematical models. By comparing the conventional sliding filter algorithm, median filter algorithm and Kalman filtering aided by machine learning, it is confirmed that Kalman filtering aided by machine learning has a better effect on reducing the standard deviation of inductive signal. The standard deviation can be quickly converged to the target value through short-term iteration, effectively eliminating the fluctuation of the standard deviation value of the inductive signal, and improving the stability of the standard deviation value of the inductive signal.

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