scholarly journals Evaluation of artificial intelligence techniques used in the diagnosis of failures in power plants

Respuestas ◽  
2020 ◽  
Vol 25 (2) ◽  
pp. 177-189
Author(s):  
Jesús Filander-Caratar ◽  
Andrés Mauricio-Valencia ◽  
Gladys Caicedo-Delgado ◽  
Cristian Chamorro
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Nuclear Power ◽  
Power Plants ◽  
Artificial Intelligence Techniques ◽  
Hybrid Techniques ◽  
Knowledge Based ◽  
Generation Plant ◽  
Power Generation Plant ◽  
Detection And Diagnosis

This article presents an evaluation about the research related to the development of computational tools based on artificial intelligence techniques, which focus on the detection and diagnosis of faults in the different processes associated with a power generation plant such as: hydroelectric, thermoelectric and nuclear power plants. Initially, the main techniques of artificial intelligence that allow the construction of intelligent systems in the area of fault diagnosis is described in a general way, techniques such as: fuzzy logic, neural networks, knowledge-based systems and hybrid techniques Subsequently A summary of the research based on each of these techniques is presented. Subsequently, the different articles found for each of the techniques are presented in tables, illustrating the year of publication and the description of the research carried out. The result of this work is the comparison and evaluation of each technique focused on the diagnosis of failures in power plants. The novelty of this work is that it presents an extensive bibliography of the applications of the different intelligent techniques in solving the problem of detection and diagnosis of failure in power plants

Fault diagnosis through Ferrography (Wear Particle Analysis)

2021 ◽  
pp. 303-322
Author(s):  
Anadi Sinha
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Wear Particle ◽  
Stage Ii ◽  
Lubricating Oil ◽  
Particle Analysis ◽  
Wear Particle Analysis ◽  
Analysis Technique ◽  
Detection And Diagnosis

The purpose of Plant Predictive Maintenance (PDM) programme is to improve Reliability of machineries through early detection and diagnosis of equipment problems, and degradation prior to equipment failure. Ferrography (Wear Particle Analysis) is one of the PDM techniques which allows detection, identification and evaluation of the degradation at the very incipient stage so that degradation is timely attended and mitigatory actions initiated. Ferrography is a Wear Particle Analysis technique based upon systematic collection and analysis of sample of lubricating oil from rotating and reciprocating machines. Ferrography analysis is conducted in 2 phases: Stage I – Quantitative, and Stage II – Qualitative. After Stage II analysis, recommendation is issued based on wear rating (Normal, Marginal, or Critical) so that operator can take timely action. Presently, 21 Nuclear Power Plants are operational in India and Forced Shutdown is a very costly affair. Lube oil of around 60 equipment from Indian Nuclear Power Plants is examined quarterly for Ferrography analysis, and failure of several equipment is avoided due to timely action. This paper will elaborate on the basic principles of Ferrography, and how systematic implementation of Ferrography has helped in avoiding forced failure of equipment, and hence prevent Forced Shutdown.

Applying Artificial Intelligence to Financial Investing

2018 ◽  
pp. 1-14
Author(s):  
Hayden Wimmer ◽  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Language Processing ◽  
Credit Scoring ◽  
Bankruptcy Prediction ◽  
Asset Valuation ◽  
Artificial Intelligence Techniques ◽  
Academic Literature ◽  
Market Inefficiencies ◽  
Knowledge Based

Artificial intelligence techniques have long been applied to financial investing scenarios to determine market inefficiencies, criteria for credit scoring, and bankruptcy prediction, to name a few. While there are many subfields to artificial intelligence this work seeks to identify the most commonly applied AI techniques to financial investing as appears in academic literature. AI techniques, such as knowledge-based, machine learning, and natural language processing, are integrated into systems that simultaneously address data identification, asset valuation, and risk management. Future trends will continue to integrate hybrid artificial intelligence techniques into financial investing, portfolio optimization, and risk management. The remainder of this article summarizes key contributions of applying AI to financial investing as appears in the academic literature.

Engineering of Experience Based Trust for E-Commerce

2010 ◽  
pp. 342-367 ◽  
Author(s):  
Zhaohao Sun ◽  
Jun Han ◽  
Dong Dong ◽  
Shuliang Zhao
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Research And Development ◽  
Multiagent Systems ◽  
System Architecture ◽  
Intelligent Systems ◽  
Multiagent System ◽  
Theoretical Foundation ◽  
Knowledge Based ◽  
Trust Inference

Trust is significant for sustainable development of e-commerce and has received increasing attention in e-commerce, multiagent systems (MAS), and artificial intelligence (AI). However, little attention has been given to the theoretical foundation and intelligent techniques for trust in e-commerce from a viewpoint of intelligent systems and engineering. This chapter will fill this gap by examining engineering of experience-based trust in e-commerce from the viewpoint of intelligent systems. It looks at knowledgebased trust, inference-based trust and their interrelationships with experience-based trust. It also examines scalable trust in e-commerce. It proposes a knowledge based model of trust in e-commerce and a system architecture for METSE: a multiagent system for experience-based trust in e-commerce. The proposed approach in this chapter will facilitate research and development of trust, multiagent systems, e-commerce and e-services.

scholarly journals Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 194315-194332
Author(s):  
Chao Lu ◽  
Jiafei Lyu ◽  
Liming Zhang ◽  
Aicheng Gong ◽  
Yipeng Fan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Systemic Review ◽  
Level Design

Knowledge-based Noise Analysis: A Promising Tool for Early Failure Detection in Nuclear Power Plants

1991 ◽  
Vol 24 (6) ◽  
pp. 383-390
Author(s):  
J. Bokor ◽  
A. Edelmayer ◽  
A. Soumelidis ◽  
M. Tanyi ◽  
P. Gáspár ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Noise Analysis ◽  
Failure Detection ◽  
Early Failure ◽  
Knowledge Based ◽  
Promising Tool

scholarly journals An Integrative Framework for Artificial Intelligence Education

2019 ◽  
Vol 33 ◽  
pp. 9670-9677 ◽  
Author(s):  
Pat Langley
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
System Integration ◽  
Intelligent Systems ◽  
Control Plan ◽  
Integrative Framework ◽  
Plan Generation ◽  
Knowledge Based ◽  
Intelligent Behavior ◽  
Intelligence Education

Modern introductory courses on AI do not train students to create intelligent systems or provide broad coverage of this complex field. In this paper, we identify problems with common approaches to teaching artificial intelligence and suggest alternative principles that courses should adopt instead. We illustrate these principles in a proposed course that teaches students not only about component methods, such as pattern matching and decision making, but also about their combination into higher-level abilities for reasoning, sequential control, plan generation, and integrated intelligent agents. We also present a curriculum that instantiates this organization, including sample programming exercises and a project that requires system integration. Participants also gain experience building knowledge-based agents that use their software to produce intelligent behavior.

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