## 1 Introduction  
A system's general performance as a result of its availability, dependability, and quality is known as its effectiveness [1]. System effectiveness and resilience issues related to equipment failure caused by deterioration due to usage and environmental factors can have disastrous results, leading to costly consequences, such as injuries, deaths, asset destruction, and reputational losses [2]. One way of enhancing system performance is maintenance, which lies at the intersection of a system's effectiveness and resilience. The purpose of maintenance is to reduce deterioration and increase the accessibility of functional systems. Cleaning, fixing, and replacing are all forms of maintenance. Depending on the industrial sector (between 10% and 40% for the atomic energy industry and between 9% and 20% for the chipper industry in Estonia), maintenance costs represent a significant portion of the total cost of the items produced [3]. Therefore, an efficient maintenance plan must be created in order to reduce system downtime and operating expenses, involving the monitoring, control, and maintenance of equipment and machinery in industrial environments (essential activities) to optimize the operation of a plant and avoid unnecessary failures, problems, or losses. Traditional corrective maintenance techniques are designed to fix defects when a fault is detected, and preventive maintenance (PvM) techniques, intended to reduce failures through planned control based on past lifetime data, are well-known strategies. PvM typically forecasts when a system will fail, giving rise to approaches such as the block replacement policy and the age replacement policy [4]. These maintenance techniques are being swapped for more effective ones, such as predictive maintenance (PdM), which allows the monitoring and control of equipment [5; 6]. The potential for data-driven decision-making has increased in the past 10 years due to the proliferation of sensors and tracking devices caused by the advent of the Industry 4.0 paradigm (I4.0), particularly in industrial contexts [7]. Thanks to the implementation of cutting-edge techniques enabled by I4.0 technologies [8], sensors can be incorporated into different parts of industrial plants through Internet of Things (IoT) technology. Continuous monitoring techniques are capable of obtaining and transmitting real-time information on the state of equipment and machines. Such data processes allow the implementation of PdM techniques. Machine learning (ML) algorithms are becoming the preferred approach to developing new predictive maintenance techniques [9; 10]. This type of maintenance strategy can automatically diagnose a machine's state, detect anomalies, and evaluate its useful life [11; 12]. This technique also assists in  
* Making accurate reports on the current status of equipment;
* Designing control panels for monitoring equipment;
* Improving responsiveness to process anomalies;
* Implementing automatic alarms in case of abnormal behavior. The widespread availability of degradation data provoked a series of recent studies on PdM (part of condition-based maintenance (CBM)), which has been found to be more effective than traditional PvM procedures [4; 13; 14]. Data-based models enable the training of algorithms capable of analyzing real-time data and making increasingly accurate predictions. The information provided by sensors is discontinuous and irregular. Therefore, the critical challenge of data processing and cleaning, as well as dealing with irregular data sampling, pattern detection, resampling to achieve a homogeneous frequency, and creating appropriate data structures for use in ML, must be overcome in the modeling process. The type of ML algorithm used varies depending on the requirements and the information to be predicted [15]. Prescriptive maintenance (PsM) expands on PdM by prescribing a course of action (recommending operating and maintenance decisions to prevent future equipment degradation and failure) based on historical and incoming real-time data.