Physics

Organic chemistry

Feature (linguistics)

Block (permutation group theory)

Mathematics

Static analysis

Operating system

Algorithm

Data mining

State variable

Mathematical analysis

Philosophy

Artificial intelligence

Component (thermodynamics)

Variable (mathematics)

Chemistry

Programming language

Geometry

Scale (ratio)

Layer (electronics)

Computer science

Linguistics

Independent component analysis

Quantum mechanics

Process (computing)

Thermodynamics

IEEE Transactions on Industrial Informatics

Due to the complex static, dynamic, and large-scale characteristics for modern industrial processes, in this article, we propose a double-layer distributed monitoring approach based on multiblock slow feature analysis and multiblock independent component analysis. To this end, the processed dataset is divided into the static and dynamic blocks on the basis of the sequential information of each variable in the first layer. Considering the correlations between the variables in the large-scale processes, the sequential correlation matrices in two blocks are calculated, which serves as the second-layer block division rule. Then, the static and dynamic blocks are further divided into several static and dynamic subblocks in which the variables in each subblock are strongly correlated and in the same state. The slow feature analysis and independent component analysis monitoring models are, respectively, generated for the dynamic and static subblocks. Finally, the monitoring results in each subblock are integrated by Bayesian inference to get the final statistics. The average fault detection rate of the proposed method for the Tennessee Eastman process is 0.842, while those of the other traditional methods are lower than 0.75, which shows the advantages of the proposed method.

Double-Layer Distributed Monitoring Based on Sequential Correlation Information for Large-Scale Industrial Processes in Dynamic and Static States