A Review of Deep Learning-Based Anomaly Detection Strategies in Industry 4.0 Focused on Application Fields, Sensing Equipment, and Algorithms

Liso, Adriano; Cardellicchio, Angelo; Patruno, Cosimo; Nitti, Massimiliano; Ardino, Pierfrancesco; Stella, Ettore; Renò, Vito

doi:10.1109/access.2024.3424488

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2024

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Cited by 2 publications

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Comparison of the Statistical and Autoencoder Approach for Anomaly Detection in Big Data

Mali

2024

2024 5th International Conference on Big Data Analytics and Practices (IBDAP)

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Comparison of the Statistical and Autoencoder Approach for Anomaly Detection in Big Data

Mali

2024

2024 5th International Conference on Big Data Analytics and Practices (IBDAP)

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Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis

Espina-Romero,

Gutiérrez Hurtado,

Ríos Parra

et al. 2024

Sci

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This study explores the evolution and impact of research on the challenges and opportunities in the implementation of artificial intelligence (AI) in manufacturing between 2019 and August 2024. By addressing the growing integration of AI technologies in the manufacturing sector, the research seeks to provide a comprehensive view of how AI applications are transforming production processes, improving efficiency, and opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, and thematic trends. Data were collected from Scopus, and a detailed review of key publications was carried out to identify knowledge gaps and unresolved research questions. The results reveal a steady increase in research related to AI in manufacturing, with a strong focus on automation, predictive maintenance, and supply chain optimization. The study also highlights the dominance of certain institutions and key authors driving this field of research. Despite the progress, significant challenges remain, particularly regarding the scalability of AI solutions and ethical considerations. The findings suggest that while AI holds considerable potential for the manufacturing industry, more interdisciplinary research is needed to address existing gaps and maximize its benefits.

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A Review of Deep Learning-Based Anomaly Detection Strategies in Industry 4.0 Focused on Application Fields, Sensing Equipment, and Algorithms

Cited by 2 publications

References 82 publications

Comparison of the Statistical and Autoencoder Approach for Anomaly Detection in Big Data

Comparison of the Statistical and Autoencoder Approach for Anomaly Detection in Big Data

Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis

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