## 1 Introduction  
In the fourth industrial revolution, companies are constantly looking for ways to increase production and reduce costs, especially those deriving from maintenance, which amounts to about one-third of total operating costs [1]. In this scenario, data science is having great success because it can be leveraged to extract valuable information from data that companies can exploit to their advantage to expand their business and evolve [2]. However, not all companies are still able to carry out this process, and knowing how to maximize the employment of self-learning models represents one of the main issues linked to the process of extracting information from data [3]. Data science [4, 5] is giving remarkable results in various scientific fields, such as materials science [3], maintenance management [6], programming [7] and improving the quality of a product or service performed [8]. Thanks to the continuous development of hardware, cloud systems, and new technologies, there has been an incentive for the increasingly frequent use of machine learning techniques [9]. This phenomenon is also supported by:* The growing availability of open source software packages that make the implementation of self-learning models simple [10; 11];
* The improvement of the availability of industrial data, big data, and servers in the data processing phase [12; 13]. Furthermore, within the manufacturing systems, new, reliable and secure human-centered architectures have recently appeared at the heart of the evolution towards Industry 5.0 [14]. The employment of data processing techniques to carry out predictive analysis of failures in the industrial sector has become a particularly hot topic due to the need to obtain a maintenance strategy that guarantees the availability of the systems and prevents unscheduled downtime due to sudden failures [2; 3]. Furthermore, in very competitive environments, it is necessary to better manage delivery times and reduce costs associated with non-compliant products that must be reprocessed, if possible, or thrown away [15]. During the life cycle of an industrial machine, its components could be damaged and broken, leading to high costs that companies have to face. For this reason, it is important to diagnose the roots of failure over time. Traditionally, these diagnoses have been made based on criteria (manual or knowledge-based) established by experts, but sometimes they may not work. It follows the need to study the data collected by the machines using sensors and identify possible operating anomalies to predict an imminent failure and intervene to avoid the consequences [16]. There are several types of historical components in petrochemical plants. For example, we could have rotating machines such as pumps and compressors. For these, in the literature, we have examples of failure prediction approaches that exploit data extracted from different sensors to provide adequate information for failure prediction [1; 9; 10; 16; 17; 18; 19]. In the literature, there are three maintenance approaches [1; 20]:  
* Run to failure (R2F). This strategy is based on post-failure interventions and is therefore the simplest of the maintenance approaches. Its use is justified when the consequences of the failure lead to the replacement of the single component without causing damage to the adjacent equipment and, more importantly, the failure must not have an impact on the environment and human safety. * Preventive maintenance (PvM). In this strategy, we intervene through a plan of periodic corrective actions. This largely avoids breakdowns, but unnecessary maintenance is often carried out, leading to an inefficient use of resources and increased operating costs. PvM is used in cases when a failure can cause significant damage to the safety of workers and the environment. Furthermore, the consequences of the breakdown can lead to a long period of prolonged disruption and/or even serious damage to adjacent components. * Predictive maintenance (PdM).