Annals of Operations Research, Год журнала: 2025, Номер unknown
Опубликована: Март 17, 2025
Язык: Английский
Discover Artificial Intelligence, Год журнала: 2023, Номер 3(1)
Опубликована: Дек. 7, 2023
Abstract Driven by the ongoing migration towards Industry 4.0, increasing adoption of artificial intelligence (AI) has empowered smart manufacturing and digital transformation. AI enhances industry 4.0 through AI-based decision-making analyzing real-time data to optimize different processes such as production planning, predictive maintenance, quality control etc., thus guaranteeing reduced costs, high precision, efficiency accuracy. This paper explores AI-driven manufacturing, revolutionizing traditional approaches unlocking new possibilities throughout major phases industrial equipment lifecycle. Through a comprehensive review, we delve into wide range techniques employed tackle challenges optimizing process control, machining parameters, facilitating decision-making, elevating maintenance strategies within an These encompass design, recycling/retrofitting. As reported in 2022 McKinsey Global Survey ( https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review ), witnessed more than two-fold increase since 2017. contributed research last six years. Therefore, from meticulous search relevant electronic databases, carefully selected synthesized 42 articles spanning 01 January 2017 20 May 2023 highlight review most recent research, adhering specific inclusion exclusion criteria, shedding light on latest trends popular adopted researchers. includes Convolutional Neural Networks (CNN), Generative Adversarial (GAN), Bayesian Networks, Support Vector Machines (SVM) which are extensively discussed this paper. Additionally, provide insights advantages (e.g., enhanced decision making) integration with legacy systems due technical complexities compatibilities) integrating across stages operations. Strategically implementing each phase enables industries achieve productivity, improved product quality, cost-effectiveness, sustainability. exploration potential fosters agile resilient processes, keeping at forefront technological advancements harnessing full solutions improve products.
Язык: Английский
Процитировано
119
Applied Sciences, Год журнала: 2024, Номер 14(2), С. 898 - 898
Опубликована: Янв. 20, 2024
Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in real system has been destroyed, some anomalies appear so that can be performed before breakdown takes place. Using cutting-edge technologies like artificial intelligence (AI) enhances performance accuracy predictive systems increases their autonomy adaptability complex dynamic working environments. This paper reviews recent developments AI-based PdM, focusing on key components, trustworthiness, future trends. The state-of-the-art (SOTA) techniques, challenges, opportunities associated with PdM are first analyzed. integration AI into real-world applications, human–robot interaction, ethical issues emerging from using AI, testing validation abilities developed policies later discussed. study exhibits potential areas for research, such as digital twin, metaverse, generative collaborative robots (cobots), blockchain technology, trustworthy Industrial Internet Things (IIoT), utilizing comprehensive survey current SOTA opportunities, challenges allied PdM.
Язык: Английский
Процитировано
80
Journal of Manufacturing Systems, Год журнала: 2023, Номер 68, С. 376 - 399
Опубликована: Май 9, 2023
To provide direction and advice for future research on Industry 4.0 maintenance, we conducted a comprehensive analysis of 344 eligible journal papers published between 2013 2022. Our systematic literature review identifies key trends in advanced maintenance techniques the consolidation traditional concepts, which are driven by increasing adoption technologies need to optimize manufacturing systems' performance reliability. In light our findings, highlight importance addressing sustainability factors, human aspects, implementation environmental KPIs research. Building upon these insights, introduce Maintenance 5.0 framework, emphasizes integration human-centered AI-driven strategies achieving efficient sustainable Zero-Defect Manufacturing (ZDM) systems. We propose novel framework that links policies small medium-sized enterprises (SMEs) facilitate field. This work underscores bridge gap policies, enabling seamless transition SMEs towards practices, while fostering socially responsible operations.
Язык: Английский
Процитировано
62
Computer Science & IT Research Journal, Год журнала: 2024, Номер 5(5), С. 1090 - 1112
Опубликована: Май 5, 2024
The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity criticality of its assets. Traditional approaches are often reactive inefficient, leading costly downtime safety risks. However, emergence artificial intelligence (AI) predictive technologies offers a transformative solution these challenges. This paper explores role AI-driven revolutionizing management sector. leverages machine learning algorithms analyze data predict when is required before breakdown occurs. By monitoring performance real-time, AI can identify potential issues early, allowing operators take proactive actions. approach helps minimize downtime, reduce costs, improve overall reliability safety. implementation requires comprehensive strategy that includes collection, analysis, integration with existing practices. Successful adoption lead benefits for companies, including increased uptime, extended asset lifespan, enhanced operational efficiency. reviews current landscape industry, highlighting limitations traditional practices need more approach. It then examines principles maintenance, showcasing real-world examples successful implementation. Finally, discusses considerations implementing provides recommendations companies looking transform their Keywords: Transforming Equipment; Management; Oil Gas; AI-Driven; Predictive Maintenance.
Язык: Английский
Процитировано
35
Results in Engineering, Год журнала: 2024, Номер 21, С. 101823 - 101823
Опубликована: Янв. 27, 2024
The progress of our society is reflected in the building sector, which emphasises necessity constantly modifying instruments to take advantage new opportunities. An example cutting-edge technology with potential completely transform construction sector Internet Things (IoT). goal this comprehensive analysis help industry improve understanding how crucial it embrace IoT. In study, a systematic review relevant literature was conducted identify factors that contribute enhancing IoT applications industry. primary objective list and evaluate most important uses, advantages difficulties using sector. This revealed has significant by improving productivity, safety, sustainability quality across entire lifecycle. However, barriers such as data privacy cybersecurity lack standardised protocols need be addressed. concludes likely revolutionise coming years if these challenges can overcome. These findings imply firms experiment analytic tools phased use cases, whilst policy groups must collaborate on standards protocols. Although obstacles exist, strategic implementation promises major operational breakthroughs near future.
Язык: Английский
Процитировано
28
Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 202, С. 114681 - 114681
Опубликована: Июль 2, 2024
Язык: Английский
Процитировано
28
Knowledge-Based Systems, Год журнала: 2022, Номер 259, С. 110070 - 110070
Опубликована: Окт. 30, 2022
Язык: Английский
Процитировано
49
Sensors, Год журнала: 2023, Номер 23(3), С. 1409 - 1409
Опубликована: Янв. 27, 2023
Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by significant number of publications and reports devoted these problems. However, theoretical application studies indicate lack research on the systematic literature reviews surveys that would focus evolution Industry 4.0 technologies used area cross-sectional manner. Therefore, paper existing present an up-to-date content-relevant analysis this field. The proposed methodology includes bibliometric review literature. First, general was conducted based Scopus Web Science databases. Later, search performed using Primo multi-search tool following Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. main inclusion criteria included publication dates (studies published from 2012–2022), English, found selected In addition, authors focused work within scope Maintenance study. papers fields were selected: (a) augmented reality, (b) virtual (c) system architecture, (d) data-driven decision, (e) Operator 4.0, (f) cybersecurity. This resulted selection 214 most relevant investigated area. Finally, articles categorized into five groups: (1) Data-driven decision-making (2) (3) Virtual Augmented reality maintenance, (4) (5) Cybersecurity maintenance. obtained results have led specify problems trends analyzed identify gaps future investigation academic engineering perspectives.
Язык: Английский
Процитировано
36
International Journal of Critical Infrastructure Protection, Год журнала: 2023, Номер 41, С. 100599 - 100599
Опубликована: Март 8, 2023
Язык: Английский
Процитировано
32
Artificial Intelligence Review, Год журнала: 2023, Номер 56(12), С. 14663 - 14730
Опубликована: Июнь 4, 2023
Язык: Английский
Процитировано
30