Jiangsheng Li's scientific contributions

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Publications (2)

Fluid catalytic cracking process quality-driven fault detection based on partial least squares and deep feedforward neural network
Article
  • May 2023
Fluid catalytic cracking (FCC) is an important process in petroleum processing. Effective monitoring of the status and quality of FCC is vital. Accurate description of the relationship between process and quality variables is the basis of quality-driven monitoring. Many process variables affect the quality of FCC; some of these effects are linear,...
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Process monitoring using principal component analysis and stacked autoencoder for linear and nonlinear coexisting industrial processes
Article
  • Jul 2020
With the relationships between industrial process variables becoming more complex, linear and nonlinear relationships coexist in most processes, both of which should be considered simultaneously to improve monitoring effect. Focusing on this issue, the paper proposes a novel principal component analysis-stacked autoencoder (PCA-SAE) model for fault...
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Citations

... Although the above methods can solve the problem of coexistence of multiple characteristics in process data to a certain extent, there are still some problems to be solved: (1) these methods usually only consider two data characteristics, and require prior knowledge to determine the modeling methods in advance. In practical industrial processes, there are usually multiple data characteristics at the same time [25]; (2) in the process of serial feature extraction, the determination of latent variables has an impact on the performance of the model [26]; ...
Reference: A novel monitoring method based on multi-model information extraction and fusion
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