Industrial Process Fault Detection Based on Entropy Score Contribution Analysis

Abstract

Aiming at the problem of poor fault detection ability in complex industrial processes, this paper proposes a fault detection method of entropy score contribution analysis, which can better determine the number of samples with a high contribution rate and achieve good fault detection results. First, normalize the input data to obtain the Euclidean distance of each line vector; Secondly, the number of samples with a high contribution rate is determined by entropy score contribution and determine the initial threshold; Thirdly, a parameter adaptive strategy is proposed to set the threshold of statistics; Finally, fault detection is carried out on the statistical data, and the size of fault detection rate and false alarm rate is calculated. In the fourth part of this paper, we use this fault detection method to detect the faults from the Tennessee Eastman process. The simulation results show that the method proposed has more advantages than the traditional methods, and can be effectively applied to complex industrial processes.