Cited by 853Explainable Artificial Intelligence (XAI)Impact of AI and Big Data on Business and SocietyDigital Transformation in Industry

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From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Ahmed, Imran, Jeon, Gwanggil, Piccialli, Francesco·2022·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2022.3146552

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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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See all 853

Authors: Ahmed, I., Jeon, G., & Piccialli, F.
Affiliations: Institute of Management Sciences Peshawar (PK)
Incheon National University (KR)
University of Naples Federico II (IT)
ORCID: Ahmed, Imran
Jeon, Gwanggil
Piccialli, Francesco
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022-08
Volume: 18
Issue: 8
Pages: 5031-5042
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2022.3146552
OpenAlex: W4210725851
Cited by: 853
References: 88
Citations by year: 2026: 163 · 2025: 246 · 2024: 249 · 2023: 149 · 2022: 46
FWCI: 56.77
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
Funders: 4I: mixed reality, machine learning, gamification and educational for Industry (F/190130/01-03/X44)
Fondo per la Crescita Sostenibile—Sportello
Fabbrica Intelligente
PON I & C (CUP: B66G21000040005, COR: 4641138)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
Keywords: explainable AI, Industry 4.0, machine learning, deep learning, predictive maintenance, downtime reduction, responsible AI, human-centered AI, intelligent machines, industrial transformation

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

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