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From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where
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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From Explainable AI: enhancing decision-making in the detection of cyber threats (2026) · cites this paper as Ahmed et al., 2022
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Canonico, R., & Sperlí, G. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103531 · Cited by 62
Automated Pulmonary Nodule Classification and Detection Using Deep Learning Architectures
Ahmed, I., Chehri, A., Jeon, G., & Piccialli, F. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3192139 · Cited by 54
“move decisions and investigations which are earlier stood challenging or difficult to achieve [1], [2].”
BackgroundSelf-citationFeng, X., Wu, J., Wu, Y., Li, J., & Yang, W. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119169 · Cited by 62
Gongane, V. U., Munot, M. V., & Anuse, A. D. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00248-9 · Cited by 67
“The applications of AI are manifold covering wide domains like robotics, defense, industrial automation, medical diagnostics, governance, chatbots, banking sector, business, online learning platforms, judicial system, etc. [1].”
InfluentialAdnan, M., Uddin, M. I., Khan, E., Alharithi, F. S., Amin, S., & Alzahrani, A. A. (2022) · IEEE Access · doi:10.1109/access.2022.3227072 · Cited by 60
“crucial to understand the decisions and build trust in XAI systems [24], [25].”
BackgroundMachine learning for small molecule drug discovery in academia and industry
Volkamer, A., Riniker, S., Nittinger, E., Lanini, J., Grisoni, F., Evertsson, E., Rodríguez-Pérez, R., & Schneider, N. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100056 · Cited by 48
Gao, Y., Qi, J., Sun, Y., Hu, X., Dong, Z., & Sun, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111506 · Cited by 48
An explainable artificial intelligence model for predictive maintenance and spare parts optimization
Dereci, U., & Tuzkaya, G. (2024) · Supply Chain Analytics · doi:10.1016/j.sca.2024.100078 · Cited by 69
Explainable AI for industrial fault diagnosis: A systematic review
Cação, J., Santos, J., & Antunes, M. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100905 · Cited by 65
Jafari, M., Sadeghi, D., Shoeibi, A., Alinejad‐Rokny, H., Beheshti, A., López-García, D., Chen, Z., Acharya, U. R., & Górriz, J. M. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05155-6 · Cited by 49
“When a CAD system produces unexpected results in diagnosing a disease from medical data, XAI methods can help identify and fix the problem in various parts of this system [285].”
InfluentialArtificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems
Windmann, A., Wittenberg, P., Schieseck, M., & Niggemann, O. (2024) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin58382.2024.10774364 · Cited by 58
Alkhawaldeh, M. M. K., Ali, A. J., Mohammad, A. A. S., Shatnawi, T. M., Al-Momani, A. M., Alzoubi, A. E., Al-Majali, R. T., al-fugaha, Z. N. A., & Al-Hawary, S. I. S. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-56015-6_25 · Cited by 45
Intelligent Resource Adaptation for Diversified Service Requirements in Industrial IoT
Zhang, W., He, Y.-Q., Zhang, T., Ying, C., & Kang, J. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3502493 · Cited by 45
“I NDUSTRIAL Internet of Things (IIoT) integrates advanced information and communication technologies with industrial systems, propelling the arrival of the Industry 4.0 era centered on intelligent manufacturing [1], [2].”
InfluentialIndustrial Foundation Models (IFMs) for intelligent manufacturing: A systematic review
Zhao, S., Liu, S., Jiang, Y., Zhao, B. M., Lv, Y., Zhang, J., Wang, L., & Zhong, R. Y. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.06.011 · Cited by 50
Knowledge and Data Dual-Driven Fault Diagnosis in Industrial Scenarios: A Survey
Wang, Y., Shen, J., Yang, S., Han, Q., Zhao, C., Zhao, P. F., & Ren, X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3387538 · Cited by 47
“The specificities, stages, and scopes are extensively discussed in [74], but will not be covered in this article.”
Backgroundtransformative potential of Generative Artificial Intelligence (GenAI) in business
Caño-Marín, E. (2024) · ESIC MARKET Economic and Business Journal · doi:10.7200/esicm.55.333 · Cited by 53
Explainable artificial intelligence prediction of defect characterization in composite materials
Daghigh, V., Ramezani, S. B., Daghigh, H., & Lacy, T. E. (2024) · Composites Science and Technology · doi:10.1016/j.compscitech.2024.110759 · Cited by 44
LIDD-YOLO: a lightweight industrial defect detection network
Luo, S., Xu, Y., Zhang, C., Jin, J., Kong, C., Xu, Z., Guo, B., Tang, D., & Cao, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9d65 · Cited by 49
Explainable Artificial Intelligence for Resilient Security Applications in the Internet of Things
Masud, M. T., Keshk, M., Moustafa, N., Linkov, I., & Emge, D. K. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3413790 · Cited by 50
“Although some studies described the significance of XAI and its application in specific domains, such as industry, digital pathology, and medicine [13], [14], none sought to explore one of the most significant fields, namely, cyber security.”
Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review
Hashemian, H., Pető, T., Ambrósio, R., Lengyel, I., Kafieh, R., Noori, A. M., & Khorrami‐Nejad, M. (2024) · Journal of Ophthalmic and Vision Research · doi:10.18502/jovr.v19i3.15893 · Cited by 54
“[124] In this regard, it is encouraging to note that researchers are actively exploring new methods to increase model interpretability.”
Neuro-Symbolic Explainable Artificial Intelligence Twin for Zero-Touch IoE in Wireless Network
Munir, M. S., Kim, K. T., Adhikary, A., Saad, W., Shetty, S. S., Park, S.-B., & Hong, C. S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3303713 · Cited by 47
“…next-generation wireless networks such as 6G so as to enable an inherent analytical ability for characterizing the behavior of the Internet of Everything (IoE), inferring causes from the generated network data, and delivering correct processes to the right users [1], [2], [3], [4], [5], [6], [7].”
BackgroundAhmad, Z., AlWadi, B. M., R, H. K. G., Ng, B., & Nguyen, D. N. (2024) · Kybernetes · doi:10.1108/k-10-2023-2205 · Cited by 65
Explainability as the key ingredient for AI adoption in Industry 5.0 settings
Agostinho, C., Dikopoulou, Z., Lavasa, E., Perakis, Κ., Pitsios, S., Branco, R., Reji, S., Hetterich, J., Biliri, E., Lampathaki, F., et al. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1264372 · Cited by 41
Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study
Massaoudi, M., Abu‐Rub, H., & Ghrayeb, A. (2024) · IEEE Open Journal of Industry Applications · doi:10.1109/ojia.2024.3354899 · Cited by 41
“Future research should focus on developing models that not only provide accurate estimations but also offer insights into the underlying mechanisms of battery degradation and performance [140].”
BackgroundSecurity and privacy of industrial big data: Motivation, opportunities, and challenges
Anjum, N., Latif, Z., & Chen, H. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104130 · Cited by 41
AcME - accelerated model-agnostic explanations: fast whitening of the machine-learning black box
Dandolo, D., Masiero, C., Carletti, M., Dalle Pezze, D., & Susto, G. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119115 · Cited by 41
Metadata
- Authors
- Ahmed, I., Jeon, G., & Piccialli, F.
- Affiliations
- Institute of Management Sciences Peshawar (PK)
- Incheon National University (KR)
- University of Naples Federico II (IT)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-08
- Volume
- 18
- Issue
- 8
- Pages
- 5031-5042
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- 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
- 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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