Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures

• Published in: IEEE transactions on control systems technology Vol. 24; no. 4; pp. 1480 - 1487
• Main Authors: Yin, Shen, Wang, Guang, Gao, Huijun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Consumption; Control systems; Data driven; Data models; Fault detection; Faults; Gaussian distribution; Mathematical analysis; Mathematical models; Matrix decomposition; Monitoring; orthogonal projections to latent structures (O-PLS); partial least squares (PLS); process monitoring; Projection; quality-related fault detection; Systematics; Data driven; quality-related fault detection; orthogonal projections to latent structures (O-PLS); process monitoring; partial least squares (PLS)
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2015.2481318

Quality- or output-related fault detection has attracted much attention in recent years. Several approaches have been developed to solve this issue based on postprocessing schemes. However, further studies find that these methods gradually lose their functions when amplitudes of quality-unrelated faults increase; in addition, they still consume a relatively large amount of calculation load in practice. In this brief, we propose a new structure of preprocessing-modeling-postprocessing, within which modified orthogonal projections to latent structures (MOPLS) method is developed. Compared with the previous approaches, the new method significantly improves the performance of quality-related fault detection. In addition, it reduces the number of required latent variables, thus it has a quite lower computational load than the previous ones. A numerical example and the Tennessee Eastman process are used to verify the effectiveness of the proposed approach.