Fault diagnosis for blast furnace ironmaking process based on two-stage principal component analysis

Monitoring an ironmaking process is a very challenging task as it often fluctuates frequently and lacks of direct measurements. Principal component analysis (PCA) technique has been widely used in various industrial fields, mainly due to its advantage of not requiring the information about the principle knowledge of the process and faults. However, the PCA based application results in ironmaking process are still limited. In this paper, based on the dataset collected from a real blast furnace with a volume of 2 000 m3, a fault diagnosis method by incorporating the PCA technique in two stages will be presented. To overcome the adverse effects of the peak-like disturbances caused by switching between two distinct hotblast stoves, they are identified and removed from the dataset through the first-stage PCA. Experimental results show that our method outperforms the existing algorithm and the operators' monitoring in detecting the getting cold accident of the blast furnace.