Research of Insulator Fault Identification Method Based on Atlas Intelligent Computing Platform

2020 ◽  
Author(s):  
Hantao Tao ◽  
Peiyao Yan ◽  
Bing jie Bai ◽  
Bo Zhang ◽  
Yuhe Fang ◽  
...  
Keyword(s):  
Fault Identification ◽  
Intelligent Computing ◽  
Computing Platform ◽  
Identification Method

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

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.

Evolutionary heuristic with Gudermannian neural networks for the nonlinear singular models of third kind

2021 ◽  
Author(s):  
Zulqurnain Sabir ◽  
Hafiz Abdul Wahab
Keyword(s):  
Neural Networks ◽  
Differential Operators ◽  
Layer Structure ◽  
Research Work ◽  
Merit Function ◽  
Third Order ◽  
Intelligent Computing ◽  
Computing Platform ◽  
Hybrid Heuristics ◽  
Hidden Layer

Abstract The presented research work articulates a new design of heuristic computing platform with artificial intelligence algorithm by exploitation of modeling with feed-forward Gudermannian neural networks (FFGNN) trained with global search viability of genetic algorithms (GA) hybrid with speedy local convergence ability of sequential quadratic programing (SQP) approach, i.e., FFGNN-GASQP for solving the singular nonlinear third order Emden-Fowler (SNEF) models. The proposed FFGNN-GASQP intelligent computing solver Gudermannian kernel unified in the hidden layer structure of FFGNN systems of differential operators based on the SNEF that are arbitrary connected to represent the error-based merit function. The optimization objective function is performed with hybrid heuristics of GASQP. Three problems of the third order SNEF are used to evaluate the correctness, robustness and effectiveness of the designed FFGNN-GASQP scheme. Statistical assessments of the performance of FFGNN-GASQP are used to validate the consistent accuracy, convergence and stability.

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