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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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Xiao, X., Alharbi, K. A., Zhang, P., Qin, H., & Yue, X. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02665-7 · Cited by 6
“Causal inference plays a crucial role in explainable artificial intelligence (xAI) sys - tems (Chou et al., 2022) which serves as the foundation for advanced manufacturing (Ahmed et al., 2022).”
Automated Computer-Assisted Diagnosis of Pleural Effusion in Chest X-Rays via Deep Learning
Huang, Y.-Y., Lin, Y., Tsai, S.-H., Chi, T., Chen, T., Chung, S., Li, K.-C., Tu, W., Abu, P. A. R., & Chen, C. (2025) · Diagnostics · doi:10.3390/diagnostics15182322 · Cited by 4
ANN‐Based Alternative Controllers for Three‐Phase Four‐Wire Grid‐Connected NPC Inverters
Yağan, Y. E. (2025) · IET Electric Power Applications · doi:10.1049/elp2.70112 · Cited by 4
Lu, J., Lü, C., Wang, Q., He, Y., Xia, B., & Zhang, X. (2025) · ISA Transactions · doi:10.1016/j.isatra.2025.12.010 · Cited by 4
FA-STTM: A hybrid Transformer-Mamba network for motor imagery EEG classification
Gao, M., Gao, C., Yao, Z., & Zhu, W. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132481 · Cited by 4
Improving semantic similarity computation via subgraph feature fusion based on semantic awareness
Deng, Y., Bai, W., Li, J., Mao, S., & Jiang, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108947 · Cited by 4
Artificial Intelligence and Cyber Security Driven Social Innovations in Digital Adventure Tourism
Sharma, S., Gautam, A., & Tyagi, R. (2024) · International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT) · doi:10.1109/ic2sdt62152.2024.10696759 · Cited by 4
What ChatGPT Has to Say About Its Topological Structure: The Anyon Hypothesis
Planat, M. R. P., & Amaral, M. M. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6040137 · Cited by 4
Embedded Machine Learning-Based Voltage Fingerprinting for Automotive Cybersecurity
Dini, P., Zappavigna, M., Soldaini, E., & Saponara, S. (2025) · IEEE Access · doi:10.1109/access.2025.3545245 · Cited by 6
“Interpretability is a key property in safety-critical applications that is typically not associated with complex neural network models such as CNNs, RNNs, LSTMs, or Transformers [38], [39], [40].”
The implications of artificial intelligence in management consulting: A risk and barrier assessment
Tiwari, S. P. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025244 · Cited by 4
Yan, W., Liu, J., Luo, J., Liu, W., Ma, Y., & Zhang, Q. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3558792 · Cited by 4
“The motor imagery (MI)-based EEG intention recognition holds significant potential for application in BCI instrumentation and measurement [4], [5], [6], encompassing a broad spectrum of industrial, medical, and human augmentation applications [7], [8], [9].”
Explainable prediction for business process activity with transformer neural networks
Wu, B., Hong, S., & Lin, R. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02501-7 · Cited by 4
“Explainable AI (XAI) techniques help humans to understand the underlying logic by which a machine learning model makes a decision [48].”
Performance evaluation of nonlinear energy scavenging overlay networks
Le‐Thanh, T., & Ho‐Van, K. (2022) · International Journal of Communication Systems · doi:10.1002/dac.5380 · Cited by 4
“1 Compute FΥpp Υp ,FΥpp Υp 1⁄4 1 FΥpp Υp ,FΥsd Υs in (49), FΥtp d Υp ,FΥpd Υp ,K1 1⁄4 0 2 Compute K2 in (50) 3 Compute O s in (27)”
Literature Review of SMS Phishing Attacks: Lessons, Addresses, and Future Challenges
Barrera, D., Naranjo, V., Fuertes, W., & Macas, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-48855-9_15 · Cited by 4
Renewable Carbon in Industry 4.0: Toward the Sustainable Bioeconomy
Pereira, A. A., Vera, F. P. S., Coelho, H. C. P., Tessaro, Í., & Chandel, A. K. (2024) · Green energy and technology · doi:10.1007/978-3-031-51601-6_1 · Cited by 4
Social and Ethical Implications of AI in Finance for Sustainability
Kumari, R., Sharma, K., & Kumar, R. (2024) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-2881-1.ch005 · Cited by 4
Rajkumar, M., Kapula, P. R., Alekhya, V., Reddy, U., Kumar, A., & Yusupov, A. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-3550-5.ch004 · Cited by 5
Abdelouahab, A. B., Chaibi, A., Haman, S., Bentassil, Z., Elouadi, A., & Moumen, A. (2024) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/iscv60512.2024.10620117 · Cited by 4
A digital twin design methodology for control, simulation, and monitoring of fluidic circuits
Gunes, V. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14332-x · Cited by 4
“Other definitions, according to specific fields, are given in [1-4], and in [5-6], in the wide context of smart factories (and called “cyber-physical systems”).”
BackgroundEnhancing Trustworthiness in AI-Based Prognostics: A Comprehensive Review of Explainable AI for PHM
Nguyen, D. A., Nguyen, K. T. P., & Medjaher, K. (2024) · Springer series in reliability engineering · doi:10.1007/978-3-031-71495-5_6 · Cited by 4
How to engineer sustainability: from resource-wise manufacturing towards ecosystem-wide impacts
Müller, J. M., Kazantsev, N. S., Allmendinger, R., Salehi-Amiri, A., Reis, J. Z., Jaradat, S., Bártolo, H. M. C. D. R. T. G., & Bártolo, P. (2024) · Sustainability Accounting Management and Policy Journal · doi:10.1108/sampj-03-2024-0305 · Cited by 4
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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