From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where
Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, i...
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| Vydáno v: | IEEE transactions on industrial informatics Ročník 18; číslo 8; s. 5031 - 5042 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1551-3203, 1941-0050 |
| On-line přístup: | Získat plný text |
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| Abstract | Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, interpretation, diagnosis, and analysis. AI-based methodologies (especially machine learning and deep learning support manufacturers and industries in predicting their maintenance needs and reducing downtime. Explainable artificial intelligence (XAI) studies and designs approaches, algorithms and tools producing human-understandable explanations of AI-based systems information and decisions. This article presents a comprehensive survey of AI and XAI-based methods adopted in the Industry 4.0 scenario. First, we briefly discuss different technologies enabling Industry 4.0. Then, we present an in-depth investigation of the main methods used in the literature: we also provide the details of what, how, why, and where these methods have been applied for Industry 4.0. Furthermore, we illustrate the opportunities and challenges that elicit future research directions toward responsible or human-centric AI and XAI systems, essential for adopting high-stakes industry applications. |
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| AbstractList | Nowadays, Industry 4.0 can be considered a reality, a paradigm integrating modern technologies and innovations. Artificial intelligence (AI) can be considered the leading component of the industrial transformation enabling intelligent machines to execute tasks autonomously such as self-monitoring, interpretation, diagnosis, and analysis. AI-based methodologies (especially machine learning and deep learning support manufacturers and industries in predicting their maintenance needs and reducing downtime. Explainable artificial intelligence (XAI) studies and designs approaches, algorithms and tools producing human-understandable explanations of AI-based systems information and decisions. This article presents a comprehensive survey of AI and XAI-based methods adopted in the Industry 4.0 scenario. First, we briefly discuss different technologies enabling Industry 4.0. Then, we present an in-depth investigation of the main methods used in the literature: we also provide the details of what, how, why, and where these methods have been applied for Industry 4.0. Furthermore, we illustrate the opportunities and challenges that elicit future research directions toward responsible or human-centric AI and XAI systems, essential for adopting high-stakes industry applications. |
| Author | Ahmed, Imran Piccialli, Francesco Jeon, Gwanggil |
| Author_xml | – sequence: 1 givenname: Imran orcidid: 0000-0002-7751-286X surname: Ahmed fullname: Ahmed, Imran email: imran.ahmed@imsciences.edu.pk organization: Center of Excellence in Information Technology, Institute of Management Sciences, Hayatabad, Peshawar, Pakistan – sequence: 2 givenname: Gwanggil orcidid: 0000-0002-0651-4278 surname: Jeon fullname: Jeon, Gwanggil email: gjeon@inu.ac.kr organization: Department of Embedded Systems Engineering, Incheon National University, Incheon, South Korea – sequence: 3 givenname: Francesco orcidid: 0000-0002-5179-2496 surname: Piccialli fullname: Piccialli, Francesco email: francesco.piccialli@unina.it organization: Department of Mathematics and Applications "R.Caccioppoli,", University of Naples Federico II, Naples, Italy |
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| CODEN | ITIICH |
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| SubjectTerms | Algorithms Artificial intelligence Artificial intelligence (AI) cloud computing cyber-physical system Deep learning Downtime Explainable artificial intelligence explainable artificial intelligence (XAI) Fourth Industrial Revolution Hidden Markov models Industrial applications Industries Industry 4.0 Internet of Things (IoT) Machine learning Manufacturing Predictive maintenance Robots Service robots |
| Title | From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where |
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