Improving the Safety of Power Plants by Developing a Digital Twin and an Expert System for Adaptive-Predictive Analysis of the Operability of Gas Turbine Units

2020 ◽  
Author(s):  
Roman Polyakov ◽  
Eugenii Paholkin ◽  
Igor Kudryavcev ◽  
Nikolay Krupenin
Keyword(s):  
Neural Network ◽  
Power Plants ◽  
Intelligent System ◽  
General Concept ◽  
Predictive Analysis ◽  
Complex Object ◽  
Basic Principles ◽  
Digital Twin ◽  
Analysis System ◽  
Generating Equipment

Abstract The article describes general approaches to creating an intelligent system for monitoring and diagnosing the operability of energy supply facilities. The general concept of the adaptive-predictive analysis system and the construction of an artificial neural network for its use in the predictive module for predicting the type and time of failure occurrence is given. The basic principles of training a neural network for recognizing various types of failures are also given. Critical remarks of the concept of creating a digital twin of such a complex object for modeling as energy-generating equipment are given.

scholarly journals Application of Information Technologies and Programming Methods of Embedded Systems for Complex Intellectual Analysis

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 94
Author(s):  
Vitalii Emelianov ◽  
Nataliia Emelianova ◽  
Anton Zhilenkov ◽  
Sergei Chernyi
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Embedded Systems ◽  
Information Technologies ◽  
Intelligent System ◽  
Information Model ◽  
Information Storage ◽  
Metallographic Analysis ◽  
Specialized Software ◽  
Analysis System

An information model is outlined, which represents an intelligent system of metallographic analysis in the form of a set of subsystems, the interaction of which ensures the performance of metallographic analysis functions. The structure of the information storage subsystem for metallographic analysis is presented. The deployment model of an intelligent metallographic analysis system is proposed and described. The paper outlines the approach to the presentation of an expert subsystem for metallographic quality control of metals based on a neural network. The process of finding a close precedent in metallographic analysis with reference to a multilayer neural network is described. An intelligent metallographic analysis system is described, which based on proposed information model. A specialized software of an intelligent metallographic analysis system is presented. The functioning results of the developed system for processing images of steel microstructures to determine the steel quantitative parameters is presented.

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Research on visual recognition intelligent system of city brand image based on fuzzy theory and regional culture

2021 ◽  
pp. 1-9
Author(s):  
Lin Han ◽  
Lu Han
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Intelligent System ◽  
Brand Image ◽  
Fuzzy Theory ◽  
Recognition System ◽  
Market Competitiveness ◽  
Visual Identity ◽  
Fuzzy Neural ◽  
Intelligent Recognition

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

Fault diagnosis method of peak-load-regulation steam turbine based on improved PCA-HKNN artificial neural network

2021 ◽  
pp. 1748006X2110105
Author(s):  
Yifan Wu ◽  
Wei Li ◽  
Deren Sheng ◽  
Jianhong Chen ◽  
Zitao Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Power Plants ◽  
Peak Load ◽  
Clean Energy ◽  
Steam Turbines ◽  
Peak Shaving ◽  
Diagnosis Method ◽  
Load Regulation

Clean energy is now developing rapidly, especially in the United States, China, the Britain and the European Union. To ensure the stability of power production and consumption, and to give higher priority to clean energy, it is essential for large power plants to implement peak shaving operation, which means that even the 1000 MW steam turbines in large plants will undertake peak shaving tasks for a long period of time. However, with the peak load regulation, the steam turbines operating in low capacity may be much more likely to cause faults. In this paper, aiming at peak load shaving, a fault diagnosis method of steam turbine vibration has been presented. The major models, namely hierarchy-KNN model on the basis of improved principal component analysis (Improved PCA-HKNN) has been discussed in detail. Additionally, a new fault diagnosis method has been proposed. By applying the PCA improved by information entropy, the vibration and thermal original data are decomposed and classified into a finite number of characteristic parameters and factor matrices. For the peak shaving power plants, the peak load shaving state involving their methods of operation and results of vibration would be elaborated further. Combined with the data and the operation state, the HKNN model is established to carry out the fault diagnosis. Finally, the efficiency and reliability of the improved PCA-HKNN model is discussed. It’s indicated that compared with the traditional method, especially handling the large data, this model enhances the convergence speed and the anti-interference ability of the neural network, reduces the training time and diagnosis time by more than 50%, improving the reliability of the diagnosis from 76% to 97%.

OPINIE PREZESA PAA, O KTÓRYCH MOWA W ART. 36A I 39B USTAWY – PRAWO ATOMOWE JAKO PRZYKŁAD PRE-LICENCJONOWANIA OBIEKTÓW JĄDROWYCH

2021 ◽  
pp. 389-411
Author(s):  
Tomasz R. Nowacki
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Energy ◽  
Nuclear Power Plants ◽  
General Concept ◽  
Practical Significance ◽  
Investment Risk ◽  
Binding Force ◽  
Technical Factors ◽  
Energy Law

This article discusses one of the solutions adopted in the nuclear energy law, which contributes to the reduction of the investment risk. It is the so-called pre-licensing which involves the assessment of key site or technical factors at the pre-investment stage in order to avoid possible problems at the stage of investment implementation. The author analyses the Polish solutions in the context of the general concept of pre-licensing, with particular respect to: the nature of pre-licensing legal instruments (opinions), the scope and requirements of the application for an opinion, and the binding force of pre-licensing acts. The practical significance of this issue is all the greater considering governmental plans to implement nuclear power in Poland and in the light of recent activities of private entities as to the construction of smaller nuclear power plants. In the latter case, prelicensing instruments are already being exercised in practice.

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