Correlated and weakly correlated fault detection based on variable division and ICA

Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high-dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C-vine and D-vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C-vine for correlated variables and the flexibility of D-vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.

Section: Process Monitoring Methods Based On Correlation Variable Clasmentioning

confidence: 99%

Section: Process Monitoring Methods Based On Correlation Variable Clasmentioning

confidence: 99%

“…The weighted correlation measurements proposed by Li et al directly weighted MIC and DC. This paper uses a random weight method to make the measurement correlation more flexible.…”

Section: Process Monitoring Methods Based On Correlation Variable Clasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Process monitoring method based on correlation variable classification and vine copula

Cui

2020

“…Process monitoring has a wide range of applications for modern industrial production processes as it can ensure stable production safety, maintain quality stabilization, and optimize production profit . The operating conditions often vary with raw materials, manufacturing parameters, production specifications, etc., which causes various operation modes .…”

Section: Introductionmentioning

confidence: 99%

Multimode processes monitoring based on hierarchical mode division and subspace decomposition

Zhou

Shi

et al. 2018

Self Cite

To handle multimodal uncertainty and dynamics which are common in actual industrial processes, this paper proposes a multimode process monitoring method based on hierarchical mode division and subspace decomposition. First, hierarchical mode division is presented to achieve hierarchical mode information for multimode process modelling, which can improve the adaptability of industrial processes with multimodal uncertainty. Then, hierarchical subspace decomposition is presented to decompose multimode processes into multiple global subspaces and multiple local subspaces for monitoring. Lastly, the most similar mode with various hierarchical modes at the current sampling point is dynamically determined for monitoring, which considers multimodal dynamics. The simulation tests of the penicillin fermentation processes are developed to show feasibility and superiority of the proposed method.

Batch process monitoring using multiway Laplacian autoencoders

Gao

Zheng

et al. 2020

In this paper, a novel nonlinear method named multiway Laplacian autoencoder (MLAE) is proposed for batch process monitoring. Autoencoder (AE) is an effective unsupervised learning neural network for nonlinear feature extraction. Compared with traditional AEs, the proposed method has two main advantages. Firstly, traditional AEs usually ignore the local structure of the original dataset. The proposed MLAE method integrates graph Laplacian regularization to the loss function, and, thus, the local structure of the normal process data is fully considered. Secondly, the Laplacian matrix of the regularization term is constructed by an average local affinity matrix of all batch runs, which contains the information of the stochastic deviations among batches. Furthermore, two statistics, ie, H 2 and SPE statistics, are developed based on the extracted hidden representation and the retained reconstruction error. The effectiveness and advantages of the MLAE-based monitoring strategy are illustrated by a benchmark penicillin fermentation process and a real E. coli fermentation process.