scholarly journals A Fault Identification Method in Distillation Process Based on Dynamic Mechanism Analysis and Signed Directed Graph

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 229
Author(s):  
Wende Tian ◽  
Shifa Zhang ◽  
Zhe Cui ◽  
Zijian Liu ◽  
Shaochen Wang ◽  
...  
Keyword(s):  
Directed Graph ◽  
Process Simulation ◽  
Expert Knowledge ◽  
Dynamic Mechanism ◽  
Fault Identification ◽  
Mechanism Analysis ◽  
Distillation Process ◽  
Complex Network Theory ◽  
Identification Method ◽  
Signed Directed Graph

Due to the complexity of materials and energy cycles, the distillation system has numerous working conditions difficult to troubleshoot in time. To address the problem, a novel DMA-SDG fault identification method that combines dynamic mechanism analysis based on process simulation and signed directed graph is proposed for the distillation process. Firstly, dynamic simulation is employed to build a mechanism model to provide the potential relationships between variables. Secondly, sensitivity analysis and dynamic mechanism analysis in process simulation are introduced to the SDG model to improve the completeness of this model based on expert knowledge. Finally, a quantitative analysis based on complex network theory is used to select the most important nodes in SDG model for identifying the severe malfunctions. The application of DMA-SDG method in a benzene-toluene-xylene (BTX) hydrogenation prefractionation system shows sound fault identification performance.

scholarly journals Identification of Unknown Abnormal Conditions in Catalytic Cracking Process Based on Two-Step Clustering Analysis and Signed Directed Graph

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2055
Author(s):  
Juan Hong ◽  
Jian Qu ◽  
Wende Tian ◽  
Zhe Cui ◽  
Zijian Liu ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Directed Graph ◽  
Working Conditions ◽  
Industrial Production ◽  
Clustering Analysis ◽  
Catalytic Cracking ◽  
Mutual Influence ◽  
Identification Method ◽  
Signed Directed Graph ◽  
Cracking Process

There are many unknown abnormal working conditions in industrial production. It is difficult to identify unknown abnormal working conditions because there are few relative sample and experience in this field. To solve this problem, a new identification method combining two-step clustering analysis and signed directed graph (TSCA-SDG) is proposed. Firstly, through correlation analysis and R-type clustering analysis, the variables are effectively selected and extracted. Then, a two-step clustering analysis was carried out on the selected variables to obtain the cluster results. Through the establishment of the signed directed graph (SDG) model, the causes of abnormal working conditions and their mutual influence are deduced from the mechanism. The application of the TSCA-SDG method in the catalytic cracking process shows that this method has good performance for abnormal condition identification.

scholarly journals An unknown fault identification method based on PSO-SVDD in the IoT environment

2021 ◽  
Vol 60 (4) ◽  
pp. 4047-4056
Author(s):  
Erbao Xu ◽  
Yan Li ◽  
Lining Peng ◽  
Mingshun Yang ◽  
Yong Liu
Keyword(s):  
Fault Identification ◽  
Identification Method
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