## 1 Introduction  
PdM has become an essential strategy in modern industrial settings, driven by the rapid expansion of Industry 4.0 technologies. By leveraging ML techniques, PdM aims to transition from reactive to proactive maintenance strategies, reducing unexpected failures, minimizing costs, and improving overall operational efficiency. Traditional maintenance approaches, such as run-to-failure and preventive maintenance, rely on either reacting to breakdowns or performing maintenance at fixed intervals. In contrast, PdM uses real-time data analysis and ML-based predictive modeling to optimize maintenance schedules and extend equipment lifespan [1, 2]. This evolution is largely driven by AI, encompassing ML and its advanced subset, deep learning (DL). ML algorithms learn from data to make predictions or decisions without explicit programming, while DL employs multi-layered neural networks to extract features from raw unstructured data. ML techniques are typically categorized into supervised and unsupervised learning [3]. As illustrated in Figure 1, supervised learning, including support vector machines (SVMs), decision trees (DTs), and artificial neural networks (ANNs), has shown strong results in fault classification and RUL estimation, though it depends on high-quality labeled data, which is often costly or impractical in industrial settings. To address this, unsupervised methods, such as clustering algorithms and autoencoders, are increasingly applied in scenarios like anomaly detection where failure labels may be unreliable or unavailable. DL architectures like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have gained prominence for their effectiveness in handling time-series sensor data [4]. Recent studies affirm the growing effectiveness of AI in real-world PdM applications. Sensor-based monitoring, especially through vibration analysis, has become indispensable across industries. Techniques like ensemble learning, hybrid modeling, and transfer learning are also being explored to enhance generalizability and robustness across diverse operational conditions. However persistent challenges including data scarcity, labeling costs, and a lack of standardized benchmark datasets, continue to limit comparability and hinder large-scale adoption [5]. The integration of digital twin technology has further advanced predictive capabilities, enabling real-time synchronization between physical assets and their virtual counterparts. This supports dynamic simulations, fault diagnosis, and predictive modeling. By combining physics-based models with ML-driven analytics, these systems aim to improve prediction accuracy and reliability. Nonetheless, challenges like data heterogeneity, cloud-edge collaboration, and the computational demands of high-fidelity models remain barriers to adoption [6]. Data-driven maintenance strategies have been adopted across various industries, such as manufacturing, automotive, power electronics, and energy. In manufacturing, a major focus lies in the condition monitoring of rotating machinery, particularly bearings in grinding machines, which require continuous oversight to prevent catastrophic failures. DL-based methods, including CNNs and transfer learning, have demonstrated effectiveness in identifying fault patterns and predicting failures. Although traditional approaches have also been used, their reliance on manually extracted features often limits performance. Integrating ML with real-time sensor data has markedly improved fault classification and RUL estimation, although noise interference and limited data availability remain practical obstacles [7]. Figure 1: Conceptual hierarchy of AI, ML, and DL methods. In the automotive sector, PdM has been extensively applied to critical components such as engines, batteries, and suspension systems. While supervised learning models have proven effective in fault identification, their dependence on labeled data is a limitation.