Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems
- URL: http://arxiv.org/abs/2405.18580v2
- Date: Fri, 5 Jul 2024 08:02:59 GMT
- Title: Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems
- Authors: Alexander Windmann, Philipp Wittenberg, Marvin Schieseck, Oliver Niggemann,
- Abstract summary: Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning.
Despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited.
- Score: 45.31340537171788
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.
Related papers
- Comprehensive Overview of Artificial Intelligence Applications in Modern Industries [0.3374875022248866]
This paper explores the applications of AI across four key sectors: healthcare, finance, manufacturing, and retail.
We discuss the implications of AI integration, including ethical considerations, the future trajectory of AI development, and its potential to drive economic growth.
arXiv Detail & Related papers (2024-09-19T19:22:52Z) - How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions [5.6818729232602205]
It is unclear if existing RE methods are sufficient or if new ones are needed to address these challenges.
Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas.
We identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges.
arXiv Detail & Related papers (2024-09-11T11:28:16Z) - Dealing with Data for RE: Mitigating Challenges while using NLP and
Generative AI [2.9189409618561966]
Book chapter explores the evolving landscape of Software Engineering in general, and Requirements Engineering (RE) in particular.
We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems.
Book provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary.
arXiv Detail & Related papers (2024-02-26T19:19:47Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review
of Applications, Techniques, Challenges, and Future Research Directions [37.22337155095065]
This paper provides a comprehensive overview of different aspects of AI and Big Data in Industry 4.0.
We highlight and analyze how the duo of AI and Big Data is helping in different applications of Industry 4.0.
arXiv Detail & Related papers (2021-04-06T11:08:02Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.