Predictive Maintenance Control Research Articles

Readiness of artificial intelligence technology for managing energy demands from renewable sources

The use of artificial intelligence (AI) has gained tremendous popularity in recent years, and it has become ubiquitous for use in the energy sector. The newly emerging digitalised tools are reliant on the use of AI which offers seamless possibilities for improved connectivity across the energy supply chains, trade and end-use. In the near course, the integration of energy supply, demand and renewable sources into the power grid will be controlled autonomously and this will aid in swift decision-making processes. This review focuses on studies that highlight the realm of AI to benefit the energy sector as a key enabler to the growth of renewable energy sources from wind, solar, geothermal, ocean as well as hydrogen-based energy storage. The work presented here alludes to an AI based energy management approach in the context of CO2-neutral hydrogen production and storage landscape. A major intended outcome of this review is that it would allow the readers to compare their AI efforts, ambitions, state-of-the-art applications, challenges, energy efficiency optimization, predictive maintenance control and global roles in policymaking for the renewable energy sector. Finally, observations and ideas for future research, enhancements and investigations through a summary of key discussions are also made.

ARTIFICIAL INTELLIGENCE IN SUSTAINABLE ENERGY INDUSTRY: STATUS QUO, CHALLENGES, AND OPPORTUNITIES

This study focuses on the future of AI in the energy sector, examining how AI can be used to improve efficiency and sustainability in the sector. The study aims to provide a realistic baseline of AI technology that can be used to compare efforts, ambitions, new applications, and challenges around the world. We covered three main topics: (i) how AI is being used in solar and hydrogen power generation; (ii) how AI is being used in supply and demand management control; and (iii) the latest advances in AI technology. In this research we explored how AI techniques outperform traditional models in controllability, energy efficiency optimization, cyber-attack prevention, IoT, big data handling, smart grid, robotics, predictive maintenance control, and computational efficiency. Our study found that AI is becoming an important tool for a new and data-intensive energy industry, which is providing a key magic tool to increase operational performance and efficiency in an increasingly cut-throat environment. KEYWORDS: Artificial Intelligence; Renewable Energy; Energy Demand; Decision Making; Big Data; Energy Digitization

ПРОГНОЗНЫЙ МОНИТОРИНГ ТЕХНИЧЕСКОГО СОСТОЯНИЯ ПУТЕМ АНАЛИЗА СОСТАВЛЯЮЩИХ СИГНАЛА ВИБРАЦИИ ПОДШИПНИКОВ ВРАЩАЮЩИХСЯ ЭЛЕКТРИЧЕСКИХ МАШИН

Relevance In industry, any failures and unplanned downtime of machines or systems can degrade or interrupt a company's core business, potentially leading to significant penalties and irreparable loss of reputation. Existing traditional maintenance approaches (failure or routine maintenance) suffer from some assumptions and limitations, such as high prevention or repair costs and inadequate or inaccurate mathematical degradation processes. Due to the trend of intelligent manufacturing, data mining, and artificial intelligence, predictive maintenance control is proposed as a new type of maintenance paradigm only after analytical models predict certain failures or degradations. Equipment condition monitoring can reduce the likelihood of failures occurring. Successful technical diagnostics and monitoring of complex electromechanical subsystems and assemblies provides an increase in service life and reliability of the systems as a whole. Aim of research A general overview of maintenance aims and objectives, which mainly include detection and prevention of failures and malfunctions, maintaining performance within established limits, condition prediction in order to improve machine reliability and full use of its resource, cost minimization, maximization of availability/reliability and multi-criteria optimization. Object of research The object of the study is the technical condition of rolling bearings of rotating electric machines. The main defects that are characteristic of this object are analyzed. In addition, a practical and effective sequential preventive maintenance method for rotating electrical machines is proposed, consisting of four steps: feature generation and fault diagnosis selection, data dimensionality reduction and feature fusion, fault detection and diagnosis, and estimation of the remaining useful life of rotating electrical machine bearings. Vibration signal analysis (from the time domain waveform and spectrum of the original or derivative signal, characteristics can be calculated and their effectiveness in diagnosing a fault can be assessed). Research methods To verify the performance of the proposed method, bearing failure is simulated in the Simulink MATLAB module. The bearing failure is created by feeding the vibration perturbation into the shaft system. Results Based on the results of the simulation, a conclusion on the operability of the proposed method for investigating the technical condition of rotating electric machines is drawn.

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

The energy industry is at a crossroads. Digital technological developments have the potential to change our energy supply, trade, and consumption dramatically. The new digitalization model is powered by the artificial intelligence (AI) technology. The integration of energy supply, demand, and renewable sources into the power grid will be controlled autonomously by smart software that optimizes decision-making and operations. AI will play an integral role in achieving this goal. This study focuses on the use of AI techniques in the energy sector. This study aims to present a realistic baseline that allows researchers and readers to compare their AI efforts, ambitions, new state-of-the-art applications, challenges, and global roles in policymaking. We covered three major aspects, including: i) the use of AI in solar and hydrogen power generation; (ii) the use of AI in supply and demand management control; and (iii) recent advances in AI technology. This study explored how AI techniques outperform traditional models in controllability, big data handling, cyberattack prevention, smart grid, IoT, robotics, energy efficiency optimization, predictive maintenance control, and computational efficiency. Big data, the development of a machine learning model, and AI will play an important role in the future energy market. Our study’s findings show that AI is becoming a key enabler of a complex, new and data-related energy industry, providing a key magic tool to increase operational performance and efficiency in an increasingly cut-throat environment. As a result, the energy industry, utilities, power system operators, and independent power producers may need to focus more on AI technologies if they want meaningful results to remain competitive. New competitors, new business strategies, and a more active approach to customers would require informed and flexible regulatory engagement with the associated complexities of customer safety, privacy, and information security. Given the pace of development in information technology, AI and data analysis, regulatory approvals for new services and products in the new Era of digital energy markets can be enforced as quickly and efficiently as possible. Big data and AI applications. • The use of AI in the smart energy industry, including power generation, supply and demand is reviewed. • The role of AI in control and design of renewable energy and how it can revolutionize the energy industry is addressed. • Big data explosion, cyberattack prevention, smart grid, IoT, DL and ML improvement are key AI success stories. • History, challenges, strengths, and global trends of existing AI-based techniques are summarized. • AI is entering emerging markets in energy industry; has an impact on sustainable growth as cheap and clean energy.

AI's Role in Sustainable Energy

This study overlook on the perspective role of AI in the energy sector, with main focus on improving efficiency and sustainability. The role is to establish a realistic benchmark for AI technology, facilitating global efforts, ambitions, novel applications, and industry obstacles. The study focuses three main areas: The application of AI in solar and hydrogen power creation. The utilisation of AI for contribution and demand management control of energy i. ii. iii. An overview of the latest techniques in AI technology This research explores how AI techniques outperform traditional models across various domains, including controllability, energy efficiency optimization, cyber-attack prevention, IoT (Internet of Things), big data handling, smart grid management, robotics, predictive maintenance control and computational efficiency. The findings of this study underscore AI as a pivotal tool for the evolving and data intensive energy industry, empowering enhanced operational performance and efficiency within an increasingly competitive landscape. This research overlooks how AI techniques outperform traditional models across various domains, including Controllability, energy efficiency optimising, cyber-attack precaution, IoT (Internet of Things), big data handling, smart grid managing, robotics, predictive maintenance control and computational efficiency. The findings of this study underscore AI as an important tool for the evolving and data intensive energy industry, empowering enhanced operational performance and efficiency within a rapid competitive landscap