scholarly journals Predicting anomaly conditions of energy equipment using neural networks

2021 ◽  
Vol 280 ◽  
pp. 09005
Author(s):  
Anastasia Sverdlova ◽  
Artur Zaporozhets
Keyword(s):  
Predictive Models ◽  
Thermal Power ◽  
Current Data ◽  
Diagnostic Systems ◽  
Equipment Operation ◽  
Forecasting Models ◽  
Failure Statistics ◽  
Equipment Failures ◽  
Energy Equipment ◽  
Environmentally Friendly Production

In modern conditions for complex thermal power facilities, the issue of developing methods for predicting equipment failures is especially relevant. Methods based on the intellectualization of diagnostic systems and allowing to obtain predictive models based on the use of both current data received in real time from measuring equipment and retrospective information are considered promising. Intellectualization of the system in terms of the ability to learn allows to quickly adjust the parameters of forecasting models under changing conditions of equipment operation, to determine new deadlines for scheduled repairs and minimize equipment downtime. A limitation of the use of methods is the incompleteness of failure statistics, ie when equipment failures are rare or non-existent. Such diagnostics of energy equipment, especially thermal power facilities, contributes to a more environmentally friendly production.

Advances in Data Mining and Applications in Power Plants

2011 ◽  
Vol 347-353 ◽  
pp. 487-493
Author(s):  
Ang Bao ◽  
Wei Guo Pan ◽  
Wen Huan Wang
Keyword(s):  
Data Mining ◽  
Power Plants ◽  
Field Data ◽  
Thermal Power ◽  
Effective Means ◽  
Monitoring And Evaluation ◽  
Research Progress ◽  
Mining Technology ◽  
Equipment Operation ◽  
Real Time Database

Describes the theory and methods of data mining technology, and the latest research progress home and abroad. In the equipment operation of various thermal power plants, more and more field data is stored in the DCS real-time database, and there is always an abundance of knowledge hidden behind the data. Adopting the date mining technology to process and analyze these data can optimize the operation of power plants and provide effective means for monitoring and evaluation of the equipment.

scholarly journals Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity

Nanomaterials ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 124
Author(s):  
Tung X. Trinh ◽  
Jongwoon Kim
Keyword(s):  
Predictive Models ◽  
Computational Models ◽  
Experimental Studies ◽  
Mixture Toxicity ◽  
Quantitative Structure Activity Relationship ◽  
Toxicity Assessment ◽  
Current Data ◽  
Data Availability ◽  
Concentration Addition ◽  
Living Organisms

Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.

scholarly journals Anomaly Detection of Operating Equipment in Livestock Farms Using Deep Learning Techniques

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1958
Author(s):  
Hyeon Park ◽  
Daeheon Park ◽  
Sehan Kim
Keyword(s):  
Deep Learning ◽  
Power Supply ◽  
Predictive Models ◽  
Growth Conditions ◽  
Environmental Sensors ◽  
External Temperature ◽  
Equipment Operation ◽  
Livestock Management ◽  
Learning Techniques ◽  
Radiator Temperature

In order to establish a smart farm, many kinds of equipment are built and operated inside and outside of a pig house. Thus, the environment for livestock (limited to pigs in this paper) in the barn is properly maintained for its growth conditions. However, due to poor environments such as closed pig houses, lack of stable power supply, inexperienced livestock management, and power outages, the failure of these environment equipment is high. Thus, there are difficulties in detecting its malfunctions during equipment operation. In this paper, based on deep learning, we provide a mechanism to quickly detect anomalies of multiple equipment (environmental sensors and controllers, etc.) in each pig house at the same time. In particular, environmental factors (temperature, humidity, CO2, ventilation, radiator temperature, external temperature, etc.) to be used for learning were extracted through the analysis of data accumulated for the generation of predictive models of each equipment. In addition, the optimal recurrent neural network (RNN) environment was derived by analyzing the characteristics of the learning RNN. In this way, the accuracy of the prediction model can be improved. In this paper, the real-time input data (only in the case of temperature) was intentionally induced above the threshold, and 93% of the abnormalities were detected to determine whether the equipment was abnormal.

Analysis of the Machine Tool Equipment as a Segment of Risk Management

2021 ◽  
pp. 64-68
Author(s):  
N.Ya. Samchuk-Khabarova ◽  
◽  
V.L. Gaponov ◽  
Keyword(s):  
Machine Tool ◽  
Injury Rate ◽  
Life Cycle Stage ◽  
Technical Documentation ◽  
Human Machine Interaction ◽  
Maintenance Schedule ◽  
Equipment Operation ◽  
Industrial Enterprises ◽  
Occupational Risks ◽  
Equipment Failures

Despite the industrial revolutions, optimization of production processes, the use of robotics and other advances of science and technology, the share of machine-tool equipment in the execution of the plan at industrial enterprises is large. Human-machine interaction is also invariable. In this regard, the injury rate of machine operators from year to year remains above average. To manage the employee occupational risks, it is required to carefully analyze the source of the risk — the machine-tool equipment used. To manage the professional risks of a machine operator, it is required to assess the following parameters of the machine-tool complex: traumatic factors in accordance with the current legal requirements, and the data obtained as a result of the equipment practical application; completeness and content of the technical documentation for the machine-tool equipment; equipment life cycle stage, equipment operation period; fulfillment of the scheduled preventive maintenance schedule; the number and nature of emergency equipment failures; ergonomic indicators of the machine. The analysis carried out according to the specified parameters can be presented visually in the form of a diagram reflecting the condition of machine equipment on six elements and on three levels using generally accepted signal colors: green - corresponds, yellow — partially corresponds, red — does not correspond. Thus, the results of the conducted analysis are visualized on a color chart, and mathematically evaluated as a percentage. Comprehensive assessment of the state of machine-tool equipment makes it possible to more efficiently determine the risks for those working on the machine-tool equipment, as well as develop measures for the modernization or replacement of the machine-tool park.

scholarly journals Vertical steam generators tube bundles vibrations

2020 ◽  
Author(s):  
С.М. Каплунов ◽  
Г.Б. Крыжевич ◽  
Т.Н. Фесенко ◽  
Е.А. Дронова
Keyword(s):  
Experimental Studies ◽  
Power Equipment ◽  
Dynamic Stresses ◽  
Spacer Grids ◽  
Equipment Operation ◽  
Contact Loads ◽  
Operational Characteristics ◽  
Tube Bundles ◽  
Equipment Failures ◽  
Vibration Parameters

Представлена реализация расчетного метода для определения и анализа параметров вибрации трубных пучков парогенераторов (ПГ), обтекаемых поперечным турбулентным потоком жидкого теплоносителя. В связи с известными случаями выхода из строя энергетического оборудования вследствие истирания труб ПГ в дистанционирующих решетках, решение данной задачи весьма актуально в настоящее время. Экспериментальные исследования конструкций такого типа чрезвычайно дороги и трудоемки. В связи с этим целью работы является разработка и создание современных математических моделей вибрации трубных пучков. Достижение такой цели представляется весьма прогрессивным и важным для повышения ресурса и безопасности эксплуатации современного энергетического оборудования. Полученные в результате последующих исследований зависимостей параметров вибрации, а именно амплитуды, частотного состава, динамических напряжений, контактных нагрузок и пути скольжения труб в дистанционирующих решетках от конструкционных и эксплуатационных характеристик и параметров ПГ были подробно исследованы. Приведенный анализ подтвердил предполагаемое существенное влияние данных параметров на повышение вибропрочности многокомпонентной конструкции ПГ. The paper presents implementation of a computing method for determination and analysis of vibration parameters of steam generator tube bundles (SG) streamlined by cross liquid coolant turbulent flow. Due to frequent power equipment failures caused by abrasion of SG tubes in spacer grids, this problem is very relevant at the present time. Experimental studies of this type of structures are extremely expensive and time-consuming. The purpose of this paper consists in creation and generation of modern mathematical models of tube bundles vibration. Achievement of this purpose could result in increase of resource and safety of modern power equipment operation. Vibration parameters dependencies obtained in the subsequent research, including amplitudes, frequency composition, dynamic stresses, contact loads and tube sliding paths in spacer grids, depending on structural and operational characteristics of SG were carefully studied. The above analysis confirmed the expected significant Influence of these parameters on increase of multi-component SG structure vibration resistance.

scholarly journals Improving reliability of power plant equipment due to measurement information redundancy

2019 ◽  
Author(s):  
Kseniia Sapozhnikova ◽  
Iuliia Baksheeva ◽  
Roald Taymanov
Keyword(s):  
Power Plants ◽  
Thermal Power ◽  
Measuring System ◽  
Optimal Decision ◽  
Measurement Information ◽  
Equipment Operation ◽  
Intelligent Sensors ◽  
Power Plant Equipment ◽  
Plant Equipment ◽  
Multichannel Measuring

Significant features of using the equipment installed at nuclear, hydraulic and thermal power plants are a multiyear cycle of continuous operation with minimum maintenance works and very high costs necessary to eliminate the consequences of possible crashes. Automatic checking of the state of the most important equipment units can become the optimal decision. In Russia, methods of building intelligent sensors and methods of intellectualization of multichannel measuring systems have been developed. Intellectualization of a measuring system enables to increase the reliability of equipment operation significantly. Examples are given.

scholarly journals Potato Pathogens in Russia’s Regions: An Instrumental Survey with the Use of Real-Time PCR/RT-PCR in Matrix Format

Pathogens ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 18 ◽  
Author(s):  
Alexander Malko ◽  
Pavel Frantsuzov ◽  
Maksim Nikitin ◽  
Natalia Statsyuk ◽  
Vitaly Dzhavakhiya ◽  
...  
Keyword(s):  
Large Scale ◽  
Current Data ◽  
Diagnostic Systems ◽  
Clavibacter Michiganensis ◽  
Data Set ◽  
Clavibacter Michiganensis Subsp ◽  
Wide Range ◽  
Wide Scale ◽  
Reported Data ◽  
Potato Pathogens

Viral and bacterial diseases of potato cause significant yield loss worldwide. The current data on the occurrence of these diseases in Russia do not provide comprehensive understanding of the phytosanitary situation. Diagnostic systems based on disposable stationary open qPCR micromatrices intended for the detection of eight viral and seven bacterial/oomycetal potato diseases have been used for wide-scale screening of target pathogens to estimate their occurrence in 11 regions of Russia and to assess suitability of the technology for high-throughput diagnostics under conditions of field laboratories. Analysis of 1025 leaf and 725 tuber samples confirmed the earlier reported data on the dominance of potato viruses Y, S, and M in most regions of European Russia, as well as relatively high incidences of Clavibacter michiganensis subsp. sepedonicus, Pectobacterium atrosepticum, and P. carotovorum subsp. carotovorum, and provided detailed information on the phytosanitary status of selected regions and geographical spread of individual pathogens. Information on the occurrence of mixed infections, including their composition, was the first data set of this kind for Russia. The study is the first large-scale screening of a wide range of potato pathogens conducted in network mode using unified methodology and standardized qPCR micromatrices. The data represent valuable information for plant pathologists and potato producers and indicate the high potential of the combined use of matrix PCR technology and network approaches to data collection and analysis with the view to rapidly and accurately assess the prevalence of certain pathogens, as well as the phytosanitary state of large territories.

Life cycle information support for thermal power equipment design

2021 ◽  
Vol 5 ◽  
pp. 43-51
Author(s):  
Evgeni Boiko
Keyword(s):  
Life Cycle ◽  
Information Management ◽  
Business Process ◽  
Support System ◽  
Thermal Power ◽  
Equipment Design ◽  
Information Support ◽  
Power Equipment ◽  
Suggested Approach ◽  
Equipment Operation

Development of a proactive life cycle information management and support system for thermal power equipment can reduce costs and risk of equipment operation. Developing this system at the stage of equipment design is crucial for selecting correct technical and engineering solutions for further efficiency and resilience. Business process re-engineering in design and a CAD library of models for a variety of thermal power equipment are major parts of the suggested approach.

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