A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment

Discover Artificial Intelligence, Journal Year: 2023, Volume and Issue: 3(1)

Published: Dec. 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.

Language: Английский

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Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(2), P. 898 - 898

Published: Jan. 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.

Language: Английский

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Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework

Journal of Manufacturing Systems, Journal Year: 2023, Volume and Issue: 68, P. 376 - 399

Published: May 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.

Language: Английский

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Transforming equipment management in oil and gas with AI-Driven predictive maintenance

Computer Science & IT Research Journal, Journal Year: 2024, Volume and Issue: 5(5), P. 1090 - 1112

Published: May 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.

Language: Английский

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A survey-based approach of framework development for improving the application of internet of things in the construction industry of Malaysia

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101823 - 101823

Published: Jan. 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.

Language: Английский

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Digital technologies for a net-zero energy future: A comprehensive review

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 202, P. 114681 - 114681

Published: July 2, 2024

Language: Английский

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Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 22(1), P. 1920 - 1929

Published: April 30, 2024

Offshore platforms are vital assets for the oil and gas industry, serving as primary facilities exploration, extraction, processing. Maintenance logistics plays a crucial role in ensuring these operate efficiently safely. However, remote harsh environments of offshore present significant challenges maintenance activities. Traditional strategies often struggle to meet demands environments, leading inefficiencies, increased costs, potential safety risks. This review discusses application Artificial Intelligence (AI) optimizing on platforms. Current involve combination preventive, predictive, corrective approaches. Preventive schedules regular inspections replacements based predetermined intervals, while predictive utilizes data analytics predict equipment failures plan activities accordingly. Corrective addresses issues they arise, response unexpected failures. AI offers opportunities enhance by leveraging advanced analytics, machine learning, optimization algorithms. AI-enabled can analyze vast amounts from sensors, historical records, environmental factors forecast with greater accuracy. allows proactive planning, minimizing downtime reducing costs. Furthermore, optimize improving resource allocation scheduling. Through real-time monitoring analysis, systems prioritize tasks urgency, criticality, availability. ensures that crews deployed efficiently, idle time overall productivity. Future innovations include integration Internet Things (IoT) devices autonomous systems. IoT sensors provide condition factors, enabling more precise models. Autonomous robots equipped algorithms perform routine minor repairs, need human intervention hazardous environments. implementing also poses challenges, including quality, cybersecurity, workforce readiness. Ensuring accuracy reliability is effective models, requiring robust collection management processes. Cybersecurity measures must be strengthened protect malicious attacks could disrupt operations or compromise safety. Additionally, training education essential prepare personnel working alongside interpreting AI-generated insights. Optimizing benefits terms efficiency, cost savings, By technologies, current enhanced, future revolutionize practices, making sustainable resilient face evolving challenges.

Language: Английский

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End-to-end lifecycle machine learning framework for predictive maintenance of critical equipment

Enterprise Information Systems, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

The industrial adoption of data-driven predictive maintenance (PdM) is increasing, with machine learning (ML) methods playing a key role in preventing equipment failures. However, ML models assume stationary data, condition rarely met non-stationary environments. This paper proposes comprehensive framework for managing systems PdM to address concept drift and maintain performance throughout their lifecycle, particularly during usage maintenance. includes dual-level detection, severity quantification, integration human expertise, end-to-end lifecycle management, offering robust solution long-term reliability adaptability dynamic settings.

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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Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research Frontiers

Computers, Journal Year: 2025, Volume and Issue: 14(3), P. 93 - 93

Published: March 6, 2025

Machine learning (ML) and deep (DL), subsets of artificial intelligence (AI), are the core technologies that lead significant transformation innovation in various industries by integrating AI-driven solutions. Understanding ML DL is essential to logically analyse applicability identify their effectiveness different areas like healthcare, finance, agriculture, manufacturing, transportation. consists supervised, unsupervised, semi-supervised, reinforcement techniques. On other hand, DL, a subfield ML, comprising neural networks (NNs), can deal with complicated datasets health, autonomous systems, finance industries. This study presents holistic view technologies, analysing algorithms application’s capacity address real-world problems. The investigates application which techniques implemented. Moreover, highlights latest trends possible future avenues for research development (R&D), consist developing hybrid models, generative AI, incorporating technologies. aims provide comprehensive on serve as reference guide researchers, industry professionals, practitioners, policy makers.

Language: Английский

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