A method of Fault Diagnosis of non-Gaussian Property and Performance Correlation Based on Independent Component Analysis

Abstract

In industrial processes, it is critical to detect and diagnose failures, process failures, and other abnormal events to achieve safe, efficient operations. In this paper, a non-Gaussian correlation algorithm based on independent component analysis is proposed to monitor the non-Gaussian process variables and non-Gaussian performance variables. First, non-Gaussian information is extracted from the original data center by independent component analysis (ICA). On this basis, the non-gaussian information is divided into non-Gaussian performance-related subspace and non-Gaussian process-related subspace by canonical correlation analysis (CCA). The proposed method can effectively analyze the influence of disturbance and control actions on performance variables under non-gaussian data, and improve the monitoring efficiency of non-gaussian process variables. Finally, a case study is used to illustrate the applicability and effectiveness of this method.