Fault Diagnosis in Industrial Processes by Maximizing Pairwise Kullback-Leibler Divergence

• Published in: IEEE transactions on control systems technology Vol. 29; no. 2; pp. 780 - 785
• Main Authors: Lu, Qiugang, Jiang, Benben, Harinath, Eranda
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Correlation; Data-driven method; Dimensionality reduction; Fault diagnosis; Feature extraction; Iterative algorithms; Kullback–Leibler (KL) divergence; Machine learning; Monitoring; Outliers (statistics); Principal component analysis; Process control; process monitoring; Tennessee Eastman Process (TEP)
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2019.2950403

Fault diagnosis gains increasing attention for its ability to enhance process safety and efficiency. This brief proposes a maximized ratio divergence analysis (MRDA) approach for fault diagnosis, which maximizes the pairwise ratio Kullback-Leibler (KL) divergence between each pair of classes during the dimensionality reduction step. In addition, an iterative algorithm based on deflation techniques is put forward for learning the loading vectors of MRDA. The proposed MRDA-based approach allows for improved power of fault diagnosis because of the following advantages over classical monitoring methods. First, MRDA maximizes the pairwise ratio divergence between each pair of classes, which directly leads to enhanced classification performance in the low-dimensional space. Moreover, MRDA is less likely to be dominated by "outlier" classes, since its objective is an average of ratio divergence, thereby facilitating the proposed method to be beneficial to the classification of imbalanced faulty classes. The effectiveness of the MRDA-based approach for fault diagnosis is verified by the Tennessee Eastman process (TEP).