Euclidean-Riemannian Joint Low-Rank Projections for Industrial Process Monitoring - Fu, Yuanjian; Luo, Chaomin; Xu, Xue (Industrial & engineering chemistry research, 2024)
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.