## 2 Materials and Methods
### IIoT Background and the Potential of GenAI-Driven DT  
IIoT is revolutionizing industrial sectors by connecting machines, sensors, and devices through networks, enabling real-time data acquisition and intelligent decision-making. Industrial IoT can be used for process monitoring [36; 37]. It emphasizes predictive maintenance, process optimization, and asset management, leveraging advanced analytics on massive data sets collected from industrial operations. DTs, acting as virtual replicas of physical systems, leverage IIoT data to model, simulate, and monitor performance [38; 39; 40; 41]. By integrating IIoT and GenAI, enterprises can unlock new, higher levels of operational intelligence and decision-making agility. GenAI introduces a new dimension to DTs by enabling them to learn, predict, and generate new data scenarios, significantly increasing their accuracy and usability. Unlike traditional DTs that rely on fixed data patterns, generative DTs powered by AI can model complex, nonlinear systems and propose innovative solutions. This capability is especially transformative in industries such as manufacturing, energy, and transportation, where high variability and system complexity require dynamic adaptation. GenAI can simulate how machines will operate under unprecedented conditions, reducing downtime and mitigating risk. It also enables real-time scenario analysis, helping industries to adapt to disruptions very quickly or optimize resource allocation. Additionally, these intelligent DTs facilitate sustainable practices by modeling energy efficiency and predicting the carbon footprint of industrial operations. This convergence may redefine industry standards, supporting innovation and resilience in an era of rapid technological advancement, not only within the Industry 4.0 and Industry 5.0 paradigms but also their next generations [42].  
Recent publications on GenAI in AI-based DTs for fault diagnosis and predictive maintenance in Industry 4.0/5.0 show the growing focus on increasing predictive accuracy and real-time decision-making. Researchers are increasingly exploring the integration of GenAI with IoT, edge computing, and cloud-based architectures to improve system responsiveness. Many studies emphasize the use of synthetic data generation to address the problem of limited failure data in industrial environments. There is a noticeable shift towards explainable AI (XAI) to improve transparency and trust in AI-based maintenance  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \multicolumn{2}{c}{**Parameter/Feature**} & \multicolumn{1}{c}{**Value**} \\ \hline Leading types of publication & Conference review (50.0\%), article (16.7\%), conference paper (33.3\%) \\ \hline Leading areas of science & Computer science (50.0\%), Engineering (20.0\%), Mathematics (20.0\%), Materials Science (10.0\%) \\ \hline Leading topics & Industrial: Design and Manufacturing \\ \hline Leading countries & Bulgaria, Germany \\ \hline Leading scientists & [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] \\ \hline Leading affiliations & University of Architecture, Civil Engineering and Geodesy, Sofia, Bulgaria, Universitat Stuttgart, Germany \\ \hline Leading funders (where information available) & None \\ \hline Sustainable development goals & Industry Innovation and Infrastructure, Responsible Consumption and Production \\ \hline \end{tabular}
\end{table}
Table 4: Summary of results of bibliographic analysis (WoS, Scopus, dblp).  
decisions. The most progressive topics include deep learning-based anomaly detection, reinforcement learning for adaptive maintenance strategies, and hybrid AI models combining physics-based and data-based approaches. Publications also emphasize the role of GenAI in enabling self-learning DTs that evolve with operational data. This trend indicates a shift towards more autonomous, scalable, and human-centric AI-based maintenance solutions in smart industries [43].