Industrial Facilities

2007 ◽  
pp. 309-322
Author(s):  
B. Mohammadioun ◽  
L. Serva
Keyword(s):  
Industrial Facilities

Lighting of Engineering Structures and Industrial Facilities New Aspects of the Topic

2018 ◽  
pp. 29-36
Author(s):  
Nikolai I. Shepetkov ◽  
George N. Cherkasov ◽  
Vladimir A. Novikov
Keyword(s):  
Fundamental Problem ◽  
Lighting Design ◽  
Aesthetic Value ◽  
Engineering Structures ◽  
Industrial Facilities ◽  
Artificial Lighting ◽  
Industrial Structures ◽  
Design Solutions ◽  
Architectural Lighting ◽  
The Aesthetic

This paper considers the fundamental problem of artificial lighting in various types and scales of industrial facilities, focusing on exterior lighting design solutions. There is a lack of interest from investors, customers and society in high­quality lighting design for industrial facilities in Russia, which in many cities are very imaginative structures, practically unused in the evening. Architectural lighting of various types of installations is illustrated with photographs. The purpose of the article is to draw attention to the aesthetic value of industrial structures, provided not only by the architectural, but also by a welldesigned lighting solution.

INFORMATION SYSTEM OF EXPERT SUPPORT OF FIRE SAFETY MANAGEMENT AT INDUSTRIAL FACILITIES

2019 ◽  
Vol 4 ◽  
pp. 21-25
Author(s):  
Nikolai Zuev ◽  
◽  
Renat Khabibulin ◽  
Evgeni Meshalkin ◽  
Boris Pranov ◽  
...  
Keyword(s):  
Information System ◽  
Fire Safety ◽  
Safety Management ◽  
Industrial Facilities ◽  
Expert Support

Methods and tools of ensuring the operational reliability of complex industrial facilities.

2020 ◽  
Vol 14 (1) ◽  
pp. 34-42
Author(s):  
A. VAZHYNSKYI ◽  
◽  
S. ZHUKOV ◽  
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Expert Systems ◽  
Residual Life ◽  
Full Potential ◽  
Scientific Publications ◽  
Specific Element ◽  
Industrial Facilities ◽  
Artificial Neural

Approaches and algorithms for processing experimental data and data obtained as a result of using modern means of measuring equipment, selecting diagnostic parameters, pattern recognition, which constitute the methodological basis for developing methods and designing tools for creating a service system for complex industrial facilities based on predicting their performance and residual life are described in submitted article. Along with classical methods, methods based on using the full potential of the modern elemental base of microprocessor technology and the use of artificial neural networks, machine learning, and "big data" are discovered. The given examples can serve as the basis for constructing a methodology for the application of the considered approaches for organizing predictive maintenance of complex industrial equipment. An analytical review of a number of scientific publications showed that the creation of new automated diagnostic systems that can increase fault tolerance and extend the life of sophisticated modern power equipment is extremely relevant. For this, various approaches are applied, based on mathematical models, expert systems, artificial neural networks and other algorithms. Summarizing the results of scientific publications, it can be argued that the implementation of a systematic approach to the organization of repair service at the enterprise requires a comprehensive solution to the following urgent problems: • monitoring is formulated as the task of interrogating sensors and collecting information necessary for further analysis; • diagnostics, it is solved as tasks of identifying informative signs with further detection and classification of failures and anomalies in data sets; • improving the accuracy of algorithms aimed at pattern recognition; • condition forecasting is the task of assessing the current and accumulated readings of monitoring systems for making decisions regarding either a specific element of the complex or the facilities. Thus, modern technology make it possible to arrange arbitrarily complex algorithms. However, to use the full potential that artificial neural networks, expert systems, and classical methods for identifying and diagnosing equipment it is necessary to have a conceptual development of the foundations of building systems for organizing maintenance and repair of complex energy equipment

Finding and Implementing Energy Efficiency Projects in Industrial Facilities

2012 ◽  
Author(s):  
Sam Aflaki ◽  
Paul R. Kleindorfer ◽  
Victor Saenz de Miera Polvorinos
Keyword(s):  
Energy Efficiency ◽  
Industrial Facilities

scholarly journals A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2839
Author(s):  
Artvin-Darien Gonzalez-Abreu ◽  
Miguel Delgado-Prieto ◽  
Roque-Alfredo Osornio-Rios ◽  
Juan-Jose Saucedo-Dorantes ◽  
Rene-de-Jesus Romero-Troncoso
Keyword(s):  
Deep Learning ◽  
Power Quality ◽  
Electrical Power ◽  
Synthetic Data ◽  
Industrial Sector ◽  
Industrial Facilities ◽  
Diagnosis Method ◽  
Neural Network Structure ◽  
Power Quality Disturbances ◽  
Disturbance Detection

Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.

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