## 2 State of the Art  
Predictive maintenance has emerged as one of the most effective approaches for improving operational efficiency and increasing equipment durability across various industrial sectors. This practice relies on a continuous monitoring of operational parameters and the use of advanced analytical models to predict equipment failures or degradation, allowing for maintenance to be carried out at the right moment before a failure occurs [4]. The main goal of predictive maintenance is to maximize equipment availability, reduce operational costs, prevent unexpected downtime, and improve system safety and reliability [5]. Predictive maintenance is characterized by a data-driven and predictive analysis approach, which differs from traditional methods such as corrective and preventive maintenance. Instead of performing maintenance at fixed intervals or waiting for equipment failure, the aim is to anticipate failures based on real-time data and analytical models, enabling precise, scheduled interventions. This type of maintenance not only reduces costs but also increases efficiency by optimizing asset lifecycles and improving overall operational performance [6]. The automation of processes and advanced data analysis have played an increasing role in predictive maintenance, with robotic process automation (RPA) and machine learning (ML) being the two most important technologies in this context. Robotic process automation (RPA) refers to the use of software to automate repetitive, rule-based tasks within computational systems. RPA is essential for collecting and processing large volumes of data from monitoring sensors, maintenance management systems, and Internet of Things (IoT) devices. Automating these processes eliminates the need for human intervention in administrative tasks, allowing operators to focus on deeper analyses and strategic decisions [7]. On the other hand, machine learning (ML), a subfield of artificial intelligence (AI), enables computer systems to learn from data and dynamically adjust as new information is acquired. In industrial settings, ML is used to analyze sensor data, monitor operational conditions, and predict failures by identifying patterns and anomalies that might go unnoticed during traditional inspections. This predictive capability contributes to a significant shift in asset maintenance, making it more effective and cost-efficient [8]. The integration of RPA and ML in predictive maintenance has brought significant benefits to various industries. A clear example of RPA application is the automation of data collection processes, where equipment condition sensors transmit real-time information to maintenance management systems. RPA can process these data and automatically forward them to maintenance teams or monitoring systems for further analysis. In terms of machine learning, neural networks are often used to predict failures by analyzing large volumes of historical data and identifying trends and patterns that indicate the onset of an impending failure [9]. However, despite these technological advancements, some limitations remain. The main issue concerns the dependence on data quality. The effectiveness of ML algorithms depends on the quality of the data used to train the models. Inaccurate or incomplete data can compromise the accuracy of predictions, leading to incorrect diagnoses. Additionally, implementing RPA and ML requires significant investments in infrastructure and the need for specialized training for system operation and maintenance [10]. Another limitation is the need to customize ML models for each type of asset and industrial environment, which can be costly and complex. Predictive maintenance, supported by RPA and ML technologies, has proven particularly beneficial in the manufacturing sector, where operational efficiency and minimizing unplanned downtime are critical to productivity. RPA plays a key role in automating administrative and operational processes, such as scheduling maintenance, sending alerts, and managing spare parts inventory.