Biblio

Principal Component Analysis (PCA) is a basic method of fault diagnosis based on multivariate statistical analysis. It utilizes the linear correlation between multiple process variables to implement process fault diagnosis and has been widely used. Traditional PCA fault diagnosis ignores the impact of faults with different magnitudes on detection accuracy. Based on a variety of data processing methods, this paper proposes a multi-level and multi-granularity principal component analysis method to make the detection results more accurate.