## 1 Introduction  
The maritime industry is one of the most crucial pillars of modern economy. To substantiate the level of impact, in 2015, 80% of merchandise trade [1] and 90% of the European Union's (EU) external trade were transported by ships, illustrating the importance of the marine sector for both the European and global markets [2]. An essential requirement of the maritime industry is the reliability and availability of vessels. Safety-oriented and environmental regulations have become more stringent during recent years, demanding increasingly higher regulation for the condition and operation of ships to achieve higher safety standards. As a result, efficient, precise, and timely maintenance is becoming of paramount importance [3]. One of the most critical problems in the maritime industry is the adoption of the maintenance schedule. Initially, corrective maintenance policies were adopted, in which maintenance was performed after the occurrence of breakdowns. Inevitably, this approach incurs high costs, downtime, and in some cases, hazardous operations. A more sophisticated approach, and currently the most common, is Preventive Maintenance (PM). In PM, a component is replaced when it is considered to have reached the end of its useful lifetime. The most widely adopted and conservative technique is to estimate the mean time to failure of a component according to experience with components of the same type. Despite a safer approach, it can result in unnecessary costs as well, particularly if a very conservative estimation is used. Moreover, this method does not guarantee a decrease in the number of breakdowns in a fleet, as the replacement of equipment can still be performed too late. Naturally, this method is based on a tradeoff between the number of failures and the lifetime estimation of the components on board. However, a favorable balance between the two is not easy to achieve, mainly due to the different operating conditions between any two vessels. For the maritime domain, a rather new approach is Condition Based Maintenance (CBM), where the actual condition of an asset (vessel) is monitored in real-time to decide the level of required maintenance [4]. While being able to achieve significant maintenance cost reductions, CBM has strict requirements, such as the use of a multisensor monitoring approach for each component onboard, due to the multiple failure modes that are manifested using different monitoring technologies [5]. As such, successful adoption of CBM is strongly connected with the use of sophisticated models that can diagnose the health status of a component based on data from sensor networks onboard. Following rapid development progress, it has been proved that Artificial Intelligence (AI) and machine learning models have numerous applications in all engineering fields [6; 7; 8; 9; 10; 11], producing remarkable results. More specifically, one prominent field in which AI-based solutions have been producing reliable and accurate results, surpassing other methodologies, is the failure detection and classification domain [12; 13; 14; 15]. Following this trend, the maritime industry is evaluating different AI-based solutions for improving the quality of their operations both in terms of performance efficiency and safety assurance [16]. Considering the wide variety of systems on-board, Machine Learning (ML) approaches that apply to the maritime sector are practically endless. Data-driven techniques and ML-related research in marine engineering are mainly featured on subjects such as energy and performance optimization of the vessel [17], albeit there are studies in which emphasis has been given on solutions regarding the task of fault detection approaches [18; 19]. Several studies have demonstrated the successful employment of ML-based approaches in maritime maintenance, to improve the maintenance strategy of marine diesel engines [20], hull condition assessment [21], or the combination of the propeller, hull, gas turbine, and gas turbine compressor [5; 22].