A study on ChatGPT for Industry 4.0: Background, potentials, challenges, and eventualities

Journal of Economy and Technology, Journal Year: 2023, Volume and Issue: 1, P. 127 - 143

Published: Aug. 24, 2023

ChatGPT is an Artificial Intelligence (AI)-powered Natural Language Processing (NLP) tool that comprehends and produces text in response to given commands. It can be adopted for various requirements, like answering our inquiries, assisting us with content creation, translating languages, more. The fourth industrial revolution, called "Industry 4.0," denotes a new production age focused on automation, digitalisation, real-time connectivity of systems. help Industry 4.0 variety ways. AI-driven process optimisation poised revolutionise by enhancing productivity, quality assurance, efficiency. For developing this paper, articles ChatGPT/ AI were identified through Scopus, ScienceDirect, Google Scholar ResearchGate. progresses due the incorporation cutting-edge technology AI, Machine Learning (ML), NLP Manufacturing operations are changing. language model becoming well-known daily use because its promising applications. In framework 4.0, it promises processes assist advancement boosting business productivity This paper studies major need 4.0. Various associated features, traits versatile competencies briefed. Finally, identifies discusses significant applications very flexible efficient method creating human-machine interfaces automatically generating text, which provides proper knowledge guidance employee. Applications include chatbots, virtual assistants, automated customer care, translation, production. future, will become effective communication automating

Language: Английский

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An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(8), P. 4971 - 4971

Published: April 15, 2023

Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time appropriate selection scope maintenance activities are necessary not only for operation industrial but also effective planning demand own resources (spare parts, people, finances). A number studies have been conducted last decade many attempts made to use artificial intelligence (AI) techniques model manage maintenance. The aim article is discuss possibility using AI methods anticipate possible failures respond them advance by carrying out an timely manner. indirect these achieve more management activities. main method applied computational analysis simulation based on real data set. results show that preventive requires large amounts reliable annotated sensor well-trained machine-learning algorithms. Scientific technical development above-mentioned group solutions should be implemented such way they can used companies equal size with different profiles. Even relatively simple as presented helpful here, offering high efficiency at low costs.

Language: Английский

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Current trends in AI and ML for cybersecurity: A state-of-the-art survey

Cogent Engineering, Journal Year: 2023, Volume and Issue: 10(2)

Published: Oct. 25, 2023

Language: Английский

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Maintenance 4.0: Optimizing Asset Integrity and Reliability in Modern Manufacturing

International Journal of Inventive Engineering and Sciences, Journal Year: 2025, Volume and Issue: 12(2), P. 18 - 26

Published: Feb. 20, 2025

The reliability of critical assets is essential for operational success and long-term sustainability in modern manufacturing. Asset Integrity Management (AIM) ensures reliability, availability, maintainability, safety (RAMS) while minimizing risks costs. Industry 4.0 technologies—such as the Internet Things (IoT), Artificial Intelligence (AI), Big Data analytics—have revolutionized maintenance strategies, enabling real-time monitoring, predictive diagnostics, data-driven decision-making. These advancements have transformed AIM, optimizing asset performance efficiency. Maintenance leverages these technologies to integrate preventive maintenance, proactive repairs, reducing costly failures, enhancing equipment productivity. This paper examines impact on focusing transition from reactive intelligent, technology-driven solutions. It highlights benefits improved efficiency, optimized schedules, cost reduction, risk mitigation, competitive manufacturing sector. Through a comprehensive literature review, this study identifies gaps aligning traditional practices with emerging proposes framework address challenges. By combining advanced digital established AIM principles, research offers strategic roadmap integrity, achieving excellence, fostering sustainable growth

Language: Английский

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Intelligent Warehouse in Industry 4.0—Systematic Literature Review

Sensors, Journal Year: 2023, Volume and Issue: 23(8), P. 4105 - 4105

Published: April 19, 2023

The development of Industry 4.0 (I4.0) and the digitization automation manufacturing processes have created a demand for designing smart warehouses to support processes. Warehousing is one fundamental in supply chain, responsible handling inventory. Efficient execution warehouse operations often determines effectiveness realized goods flows. Therefore, its use exchanging information between partners, especially real-time inventory levels, critical. For this reason, digital solutions quickly found application internal logistics enabled design warehouses, also known as Warehouse 4.0. purpose article present results conducted review publications on operation using concepts A total 249 documents from last 5 years were accepted analysis. Publications searched Web Science database PRISMA method. presents detail research methodology biometric Based results, two-level classification framework was proposed, which includes 10 primary categories 24 subcategories. Each distinguished characterized based analyzed publications. It should be noted that most these studies, authors’ attention primarily focused implementation (1) technological solutions, such IoT, augmented reality, RFID, visual technology, other emerging technologies; (2) autonomous automated vehicles Critical analysis literature allowed us identify current gaps, will subject further by authors.

Language: Английский

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Multi-Objective optimization of selective maintenance process considering profitability and personnel energy consumption

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110870 - 110870

Published: Jan. 1, 2025

Language: Английский

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CONELPABO: composite networks learning via parallel Bayesian optimization to predict remaining useful life in predictive maintenance

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Abstract Maintaining equipment and machinery in industries is imperative for maximizing operational efficiency prolonging their lifespan. The adoption of predictive maintenance enhances resource allocation, productivity, product quality by proactively identifying addressing potential anomalies through rigorous data analysis before they escalate into critical issues. Consequently, these measures strengthen market competitiveness generate favorable economic outcomes. In many applications, sensors operate at high frequencies or capture over extended periods. This work introduces CONELPABO (Composite Networks Learning via Parallel Bayesian Optimization), a framework analyzing long time series data, particularly predicting the remaining useful life system component. It uses divide-and-conquer strategy to manage exponential growth hyperparameter search space during Optimization accelerate model training 50%. Additionally, this enables deeper networks with limited resources. usefulness demonstrated two case studies, which it achieves state-of-the-art results, showing that CNN-CNN RNN-RNN architectures are highly effective time-series data. These outperform existing approaches challenge common academic focus on CNN-RNN hybrids.

Language: Английский

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Green Jobs in the Energy Sector

Energies, Journal Year: 2023, Volume and Issue: 16(7), P. 3171 - 3171

Published: March 31, 2023

This article analyzes Green Jobs (GJs) in the energy sector. GJs are naturally created processes related to implementation of Sustainable Development Goals (SDGs); this is especially visible 7th and 8th SDGs. There currently a green transition from fossil fuels renewable sources sector, mainly technological change also influences GJ creation. Despite this, there research gap self-employment definitions. The goal paper explore scientific literature collected Scopus database using qualitative approach present areas keywords adopted method Structured Literature Review (SLR), with original query Q1. retrieved data results SLR were analyzed form bibliometric maps co-occurring generated by VOSviewer software, together tables showing clusters keyword features. As result, pivotal their identified. In study, most important sector indicated. presents current state knowledge evolution subject which can be useful for both researchers practitioners. last section paper, possible new directions future studies on creation limitations its practical implications addressed.

Language: Английский

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Digital Twin Approach for Operation and Maintenance of Transportation System—Systematic Review

Sensors, Journal Year: 2024, Volume and Issue: 24(18), P. 6069 - 6069

Published: Sept. 19, 2024

There is a growing need to implement modern technologies, such as digital twinning, improve the efficiency of transport fleet maintenance processes and maintain company operational capacity at required level. A comprehensive review existing literature conducted address this, offering an up-to-date analysis relevant content in this field. The methodology employed systematic using Primo multi-search tool, adhering Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. selection criteria focused on English studies published between 2012 2024, resulting 201 highly papers. These papers were categorized into seven groups: (a) air transportation, (b) railway (c) land transportation (road), (d) in-house logistics, (e) water intermodal (f) supply chain operation, (g) other applications. notable strength study its use diverse scientific databases facilitated by tool. Additionally, bibliometric was performed, revealing evolution DT applications over past decade identifying key areas predictive maintenance, condition monitoring, decision-making processes. This highlights varied levels adoption across different sectors underscores promising future development, particularly underrepresented domains like chains transport. paper identifies significant research gaps, including integration challenges, real-time data processing, standardization needs. Future directions are proposed, focusing enhancing diagnostics, automating processes, optimizing inventory management. also outlines framework systems, detailing components functionalities essential effective findings provide roadmap innovations improvements within industry. ends with conclusions directions.

Language: Английский

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Critical factors that influence the effectiveness of facility maintenance management practice in public university buildings in Ethiopia: an exploratory factor analysis

Cogent Engineering, Journal Year: 2024, Volume and Issue: 11(1)

Published: Feb. 8, 2024

Facility maintenance management (FMM) is essential for ensuring long-term values and to sustain project goals throughout the life cycle delivery process. However, in underdeveloped nations such as Ethiopia, facility an immature underutilised process that requires a holistic intervention practical improvement. The main aim of this study was identify prioritise critical factors affect effectiveness FMM, with focus on public universities Ethiopia. Initially, total thirty-three (33) crucial variables were identified systematic literature review desk study. To collect primary data, survey research design approach utilised using questionnaires informant interviews. A seventy-five (75) data sets obtained from 180 online surveys conducting exploratory factor analysis (EFA). outcome revealed thirteen (13) attributes grouped into four practises. final four-factor model includes F1, internal processes organisation; F2, community culture, learning, growth; F3, impacts construction quality; F4, management. This indicated practises Ethiopia are require extensive enhancement. influencing highlight need comprehensive promote improved applications Further needed analyse wider range confirmatory analysis.

Language: Английский

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