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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Meher, J. R., Mishra, S., & Das, A. (2026) · Journal of Organizational Effectiveness People and Performance · doi:10.1108/joepp-04-2025-0276 · Cited by 3
A framework and application for dynamic assessment of ship equipment based on multi-source data fusion and digital twins
Song, N., Su, S., Yu, P., & Wang, H. (2026) · International Journal of Naval Architecture and Ocean Engineering · doi:10.1016/j.ijnaoe.2026.100746 · Cited by 2
Harnessing digitalisation: An assessment of the implementation of Mining 4.0 technologies in the Indian coal mining industry
Rathor, P. K. S., Shukla, G. P., & Gupte, P. (2026) · Mining Technology Transactions of the Institutions of Mining and Metallurgy · doi:10.1177/25726668261422304 · Cited by 2
Picture fuzzy risk modeling for machine vision projects. A case study in mechanical manufacturing
Thảo, N. T. P., & Duc, M. L. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131784 · Cited by 2
Algorithmic ethics in corporate contexts: Knowledge mapping for responsible management
Barreno-Alcalde, S., Delso-Vicente, A.-T., & Rivera-Heredia, A. (2026) · Journal of Management & Organization · doi:10.1017/jmo.2026.10089 · Cited by 4
Explainable AI for Employee Retention in Green Human Resource Management: Integrating Prediction, Interpretation, and Policy Simulation
Nguyen, D. C., Tenney, D., & Kongar, E. A. (2026) · Sustainability · doi:10.3390/su18062740 · Cited by 5
Interpretable bearing fault diagnosis based on frequency-spatial domain dynamic cooperative residual network
Zhao, X., Qi, X., & Zhao, J. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae5df8 · Cited by 2
“The complex nonlinear mapping of deep neural networks is weakened, and a streamlined logic of ‘feature threshold determination + physical-mechanical correlation of faults’ is adopted to balance diagnostic accuracy and interpretability in industrial scenarios [23–26].”
The AI–PI nexus: Leveraging artificial intelligence for industrial process intensification–Applications, challenges, and future potential
Segovia‐Hernández, J. G., Juárez-García, M., Núñez-López, J. M., Cossio-Vargas, E., & Sánchez‐Ramírez, E. (2026) · Chemical Engineering and Processing - Process Intensification · doi:10.1016/j.cep.2026.110841 · Cited by 4
Explainable AI for hyperspectral imaging in food quality decision support: interpretability, reliability and future directions
Zheng, R., & Kamruzzaman, M. (2026) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2026.2694489 · Cited by 2
“XAI refers to a family of AI methods designed to make the internal logic, decision-making processes, and behavior of models understandable to humans (Ahmed et al. 2022; Minh et al. 2022).”
Explainable Artificial Intelligence in Rotating Machinery Fault Diagnosis: A Comprehensive Review and Emerging Trends
Tang, S., Ren, Z., Zheng, L., & Wang, J. (2026) · Sensors · doi:10.3390/s26175379 · Cited by 2
“Furthermore, they need to adapt to stringent industrial operating conditions [18].”
MSDAR: Multi-Stage Dynamic Architecture Intrusion Detection System
Elshafee, A. M., & Azer, M. A. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130762 · Cited by 2
“To mention a few, Artificial Intelligence (AI) [1], Blockchain, cryptocurrencies [2], Internet of Things (IoT) [3], cloud computing, and wireless technology.”
Background
Regression Yield Analysis for Semiconductor Manufacturing Based on Fabrication Conditions
Lee, Y., & Roh, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020526 · Cited by 2
“Moreover, the explainable artificial intelligence (XAI) method is adapted to extract important features from the model to improve the manufacturing process [4].”
Method
Application of Machine Learning Algorithms in Online Marketing
Srivastava, A. V., Umrao, M. L. S., & Kumar, M. D. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.49585 · Cited by 3
A Survey of Industry in Cambodia and Future Prospects Industry 4.0
Cheang, V., Heng, S., Thourn, K., & Yahoui, H. (2023) · Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) · doi:10.1109/ectidamtncon57770.2023.10139624 · Cited by 2
“0 technologies which have been revolutionized from waterpower, fuel engine, electricity, and micro controllers including technologies as following as Internet of Things (IoT), cloud computing, machine learning (ML), deep learning (DL), cybersecurity, and networking [1], [2].”
Background
Exploration on the Integration of New Generation Information Technology and Curriculum Ideological and Political Under the Background of CEC Education Model
Liu, T., Jiang, H., Liu, Y., & Deng, R. (2023) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-29016-9_20 · Cited by 2
An Automation Designed for Industry 4.0 Using Robotics and Sensors that Based on IoT & Machine Learning
Soumyashree, S., Gupta, A., & Biswas, B. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202340502014 · Cited by 2
Data-driven process adjustment policies for quality improvement
Fries, N., & Rydén, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121524 · Cited by 3
Fine-Tuned SqueezeNet Lightweight Model for Classifying Surface Defects in Hot-Rolled Steel
de la Rosa, F. L., Gómez-Sirvent, J. L., Belmonte, L. M., Morales, R., & Fernández‐Caballero, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43085-5_18 · Cited by 2
Industrial Asset Management and Secure Sharing for an XAI Manufacturing Platform
Reji, S., Hetterich, J., Pitsios, S., Gkolemi, V., Perez-Castanos, S., & Pertselakis, M. (2023) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc58018.2023.10332309 · Cited by 2
“With the advancement of machine learning (ML) and deep learning (DL) algorithms, AI systems are using them to be a part of Industry 4.0 [2].”
Influential
Formal XAI via Syntax-Guided Synthesis
Bjørner, K., Judson, S., Cano, F., Goldman, D., Shoemaker, N., Piskač, R., & Könighofer, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46002-9_7 · Cited by 2
“Explainable artificial intelligence (XAI) has been receiving significant attention across multiple application domains [10, 2].”
Enhancing Maternal Outcome Prediction Using Explainable Artificial Intelligence for Women of Childbearing Age
Nwokoro, C. O., Inyang, U. G., Eyoh, I., Umoh, U. A., Onyeabochukwu, A. D., Nwokoro, K. C., Opara, P. I., Chinmanma, O., & Kingsley, J. U. (2023) · Asian Journal of Science and Applied Technology · doi:10.51983/ajsat-2023.12.2.4072 · Cited by 3
Framework for comprehensive assessment of ecological water conveyance based on long-term evolution forecast of riparian forests in arid floodplains: synergistic consideration of flood inundation and groundwater
Liu, Y., Jiang, Y., Zhang, S., Wang, D., & Chen, H. (2024) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2024.02.056 · Cited by 2
Talento humano e inteligencia artificial en el desempeño laboral en un Gobierno Regional de Perú
Gil, C. A. R. (2024) · Revista de Climatología · doi:10.59427/rcli/2024/v24cs.1322-1348 · Cited by 2
Enhanced time-sensitive networking configuration detection using optimized BPNN with feature selection for industry 4.0
Wang, C., Lin, C., Tang, C., Wang, Y., Xian, Y., Zhao, Y., Xue, H., & Huan, Z. (2024) · Cluster Computing · doi:10.1007/s10586-024-04493-5 · Cited by 2
A brief Review on the Role of Context in Explainable AI
Baruah, R. D., & Organero, M. M. (2024) · 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais58494.2024.10570037 · Cited by 2
“Various other surveys on XAI are focused towards specific domains such as healthcare [23], industry [24], cyber-security [25], and finance [26].”
Background
Force Estimation From Surface-EMG Using Element Description Method
Sodenaga, D., Takeuchi, I., Silve, D. D., & Katsura, S. (2024) · IEEE Journal of Emerging and Selected Topics in Industrial Electronics · doi:10.1109/jestie.2024.3426034 · Cited by 2
“There are many techniques for making models interpretable [28].”
Applying the ABC Approach for Edge Detection in Unshelled Banana Prawn Images
Supeesun, A., Eiamsaard, K., & Banharnsakun, A. (2024) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse61278.2024.10613726 · Cited by 2
Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations
Игоревна, С. А., Khalyasmaa, A. I., & Матренин, П. В. (2024) · Power engineering research equipment technology · doi:10.30724/1998-9903-2024-26-4-75-88 · Cited by 2
Use of AI in the UK Food System
Food Standards Agency, Zakaria, S., Bradford, M., Hutton, E., & Besse, J. (2024) · FSA research and evidence. · doi:10.46756/001c.123638 · Cited by 2
Endo-4 S RF: Learning Radiance Field for Dynamic Surface Reconstruction of Surgical Tissues With Obstacle Stealth Under Single-View and Depth-Free Monocular Endoscopy
Lu, B., He, C., Hou, W., Han, L., Wang, H., Sun, L., Jiang, Z., & Qi, P. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3516788 · Cited by 3
“F LOURISHING advancements of artiﬁcial intelligence (AI) nowadays are catalyzing technical revolutions worldwide, which dramatically expedite industry innovations and up-grade [1].”
Explainable AI in Manufacturing
Zeiser, T., Ehret, D., Lutz, T., & Saar, J. (2024) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc61926.2024.10794363 · Cited by 6
“Although the benefits of AI in production processes are well known, it is crucial that factories have a clear development plan for XAI and choose the appropriate type of industrialisation and automation platform [29], [41].”
Influential
Understanding consumer perception and acceptance of AI art through eye tracking and Bidirectional Encoder Representations from Transformers-based sentiment analysis
Yu, T., Xu, J., & Pan, Y. (2024) · Journal of Eye Movement Research · doi:10.16910/jemr.17.5.3 · Cited by 3
“In recent years, rapid advancements in AI and its growing applications in art have attracted considerable attention from academia and industry alike, driving innovation and blending traditional creative forms (Aggarwal et al, 2018; Ahmed et al., 2022; Anantrasirichai & Bull, 2022).”
Matching the Expert’s Knowledge via a Counterfactual-Based Feature Importance Measure
Alfeo, A. L., Cimino, M. G. C. A., & Gagliardi, G. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74633-8_5 · Cited by 2
Demystifying PDFs: Emerging AI for text generation and operation on PDF
Sharma, A., Baveja, M., Kanikicherla, S., Kaushik, D., Sharma, Y., & Jessica, J. (2024) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon62057.2024.10838008 · Cited by 2
“Because of their priceless training, they are able to educate the next generation by modeling the sophisticated word construction that goes into coherent sentences and encouraging an appreciation of the linguistic complexity [6].”

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

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