## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Area** & \multicolumn{3}{c}{**Identified**} \\ \cline{2-3}  & **Gap(s)** & **Possibilities of Closing Gap(s)** \\ \hline \multirow{4}{*}{Real-time data fusion and processing} & Current methods struggle to effectively integrate multimodal data (e.g., IoT sensor readings, historical records, and environmental factors) in real time to achieve predictive accuracy. & Explore scalable architectures for real-time data fusion using GenAI capabilities. \\ \hline \multirow{4}{*}{Explainability and interpretability} & The “black box” nature of many GenAI models limits trust and adoption in industrial environments. & Develop interpretable GenAI models that can explain failure prediction decisions in a way that is understandable to human operators. \\ \hline \multirow{4}{*}{Generating synthetic data for rare faults} & Industrial systems often lack sufficient labeled data for rare but critical fault types. & Explore GenAI techniques to generate high-quality synthetic datasets that mimic rare fault conditions for robust training. \\ \hline \multirow{4}{*}{Adaptive learning in changing environments} & Current models are not suited for dynamic industrial environments where machine configurations and operating conditions change frequently. & Develop an adaptive GenAI framework that can learn continuously and update DTs without complete retraining. \\ \hline \multirow{4}{*}{Integration of domain knowledge} & Many GenAI approaches neglect the integration of domain expert knowledge, leading to less reliable fault diagnosis. & Combine domain knowledge with generative models to improve fault diagnosis reliability and contextual validity. \\ \hline \multirow{4}{*}{Generalization across device(s) types} & Existing GenAI models are often tailored to specific machines and lack cross-device generalization. & Design generalized GenAI-based DTs that can transfer knowledge across different device types and configurations. \\ \hline \multirow{4}{*}{Cybersecurity in GenAI-based DTs} & Increased connectivity and dependency on GenAI increase cybersecurity vulnerability in DTs. & Develop secure GenAI frameworks that protect sensitive industrial data while maintaining predictive accuracy. \\ \hline \multirow{4}{*}{Low-power, edge-compatible solutions} & Many GenAI models are computationally intensive, making them unsuitable for edge deployments in smart factories. & Optimize GenAI algorithms for resource-constrained environments, enabling deployment on edge devices. \\ \hline \multirow{4}{*}{Multi-twin collaboration} & Collaboration between multiple DTs for complex systems or connected machines is underexplored. & Explore a framework for GenAI-enabled multi-twin ecosystems to improve fault diagnosis in connected environments. \\ \hline \multirow{4}{*}{DTs lifecycle management} & Limited research addresses long-term DT lifecycle management, such as model updates or retirement of outdated twins. & Develop methods for continuous evolution and maintenance of GenAI-based DTs to ensure continued accuracy and relevance. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research gaps observed in the state of the art in GenAI-based DTs for fault diagnosis for predictive maintenance in Industry 4.0/5.0 (own version). adaptive tools capable of scenario analysis and prescriptive insights. Consequently, this advancement pushes the boundaries of what's possible in predictive maintenance, aligning closely with the smart, automated vision of Industry 5.0 [32]. GenAI is being integrated with AI-based DTs by enhancing their ability to simulate, predict, and adapt to real-world conditions in industrial environments. This integration involves using generative models such as VAEs and GANs to create synthetic datasets that complement real-world sensor data, improving fault detection even when historical failure data are sparse.