PCA-LMNN-Based Fault Diagnosis Method for Ironmaking Processes with Insufficient Faulty Data

Abstract

Fault detection and fault classification are important in the modern ironmaking process. Some studies based on principal component analysis (PCA) techniques have been performed for fault detection in the ironmaking process. However, studies on fault classification in the ironmaking process remain limited. In this paper, problems that are related to the classification of abnormalities in blast furnaces are considered. We fuse historical abnormal data that were collected from three real blast furnaces to address the problem of insufficient historical faulty data. To extract common features for the same type of abnormalities, which are not affected by different operation points or different blast furnaces, we propose the use of a contribution vector as a fault feature, which is calculated by the PCA-based technique. The large marginal nearest neighbor (LMNN) technique is employed to train a classifier with contribution vectors as inputs. Twenty-one historical abnormalities in three different real blast furnaces are employed to validate the proposed method. The results indicate that this method achieves the desired performance.