## 1 Introduction  
The Industry 3.0 paradigm introduced automation and early diagnostics. Predictive maintenance is a method of forecasting and managing the condition of machines, devices,production lines based on historical data, mechanism models and domain knowledge that predicts equipment trends, and behavioral patterns and correlations using statistics or artificial intelligence (AI) models. This allows us to predict remaining useful life, upcoming failures, and other key indicators in advance, improving the decision-making process for maintenance activities, reducing the risks associated with failures and avoiding unnecessary equipment downtime [1; 2]. Predictive maintenance began with the advent of automation in Industry 3.0, using sensors and condition monitoring systems to track equipment performance. In this phase, techniques such as vibration analysis and thermal imaging were used along with manual data logging for trend analysis, enabling basic failure prediction. Scientists and engineers developed statistical models to predict failures based on historical data, introducing an early form of predictive maintenance. The Industry 4.0 paradigm saw the transition to continuous, real-time data collection from interconnected devices and systems, powered by the Internet of Things (IoT) and Big Data. Machine learning (ML) algorithms began to analyze massive data sets collected from IoT devices to detect patterns, predict failures, and recommend maintenance actions. Digital twins (DTs) were introduced as virtual replicas of physical assets, integrating sensor data and simulations for real-time monitoring and fault diagnosis. Predictive maintenance is related to various areas of research and economic practice [3; 4]. ML plays a crucial role in enhancing DT technology by enabling real-time data analysis, predictive modeling, and automation. DTs, which are virtual replicas of physical assets, leverage ML to process vast amounts of sensor data and detect anomalies [5]. By using predictive analytics, ML helps forecast potential failures and optimize maintenance schedules, reducing downtime and costs [6]. In manufacturing, DTs powered by ML enhance production efficiency by identifying inefficiencies and improving quality control [7]. Healthcare applications benefit from AI-driven DTs that simulate patient conditions, allowing personalized treatment planning [8]. In smart cities, ML-based DTs optimize traffic flow, energy usage, and infrastructure management [9]. The aerospace industry uses DTs to simulate aircraft performance under various conditions, ensuring better safety and efficiency [10]. ML also improves DTs in supply chain management by predicting demand fluctuations and optimizing logistics [11]. These AI-enhanced virtual models support sustainability by analyzing environmental impacts and optimizing resource usage. ML significantly enhances the capabilities of DTs, making them more accurate, intelligent, and beneficial across industries. Industry 4.0 also saw the rise in edge computing, which enabled predictive models to process data locally, reducing latency and increasing responsiveness. In the Industry 5.0 paradigm, predictive maintenance has moved towards a collaborative human-AI approach, emphasizing user-friendly tools and sustainable maintenance practices [12]. GenAI has begun to enhance DTs by simulating complex scenarios, generating synthetic data, and improving fault detection and predictive models [13]. DTs based on GenAI in Industry 5.0 focus on optimizing asset performance while adapting to environmental goals, which is the latest advancement in predictive maintenance [14]. The future implications of GenAI, large language models (LLM) and search-augmented generation (RAG) will influence practices in such distant industrial sectors as construction, but also specialist education [15]. GenAI plays a significant transformational role in AI-based DTs for fault diagnostics, driving the advancement of predictive maintenance for Industry 5.0.