Euclidean-Riemannian Joint Low-Rank Projections for Industrial Process Monitoring

• Published in: Industrial & engineering chemistry research Vol. 63; no. 13; pp. 5803 - 5812
• Main Authors: Fu, Yuanjian, Luo, Chaomin, Xu, Xue
• Format: Journal Article
• Language: English
• Published: American Chemical Society 03.04.2024
• Subjects: chemistry; data analysis; geometry; Process Systems Engineering; Tennessee; Tennessee
• ISSN: 0888-5885; 1520-5045; 1520-5045
• DOI: 10.1021/acs.iecr.3c04244

Due to industrial processes usually characterized by multiple operating units and complex interactions, process data is formulated with hybrid structures, indicating that Euclidean and non-Euclidean structures simultaneously exist among data, which poses a great challenge for process monitoring. However, existing monitoring methods tend to analyze data in terms of a single subject in which a single Euclidean metric or non-Euclidean metric is employed to represent data, which probably deteriorates the model accuracy. This paper proposes a novel method called Euclidean-Riemannian joint low-rank projections for process monitoring. A multiple structure embedding-guided learning framework, in which Euclidean space and Riemannian manifold are mapped into a common subspace, is developed to exploit the underlying information on heterogeneous data spaces. Furthermore, the low-rank constraint is exploited to alleviate the negative influence of corruption so that monitoring results become more reliable. The l 2,1 norm is forced on projection matrix, which enables the proposed approach to be more flexible in the selection of useful information. By this means, the reduced-dimensional representations captured can give more insights into the intrinsic information on data, enhancing the fault detection capability. The experimental results on the Tennessee benchmark platform and the real-world industrial processes, namely fluidized catalytic cracking process, show the effectiveness of our proposed approach.