## 1 Introduction  
The shipping industry plays a crucial role in global trade, accounting for approximately 80% of the world's freight transport and contributing 2.2% of the atmospheric air pollutants [1]. A report from the European Union in 2019 [2] indicates that, without significant intervention, the greenhouse gas (GHG) emissions from shipping could rise by 50% to 250% by the year 2050, relative to the levels recorded in 2008. To mitigate these emissions, the maritime sector has emphasized the adoption of data-driven strategies. The incorporation of digital twins (DTs) has significantly improved the operational efficiency and extended the operational lifespan of vessels [3]. However, most current methodologies focus on models that simulate the energy consumption during a ship's operation. The main engine and auxiliary systems, including the thrusters and pumps, are powered by electric induction motors. Although these motors are generally dependable, they are susceptible to damage in components such as the shaft, bearings, and stator windings. Preventive maintenance aims to reduce the likelihood of equipment failures; however, it can inadvertently lead to the early replacement of parts, which may shorten the lifespan of components and increase the overall costs. Furthermore, unexpected stresses can cause failures to occur prior to the planned maintenance schedule, potentially disrupting operations and creating safety hazards [4]. Research shows that around 32% of damage to induction motors is due to insulation failures resulting from short circuits, which produce high currents that threaten both motor integrity and operator safety [5]. This highlights the critical need for robust condition monitoring that focuses on vibrations, temperatures, and current levels to ensure the timely detection of faults ([PERSON] et al., 2018). Motor current signature analysis is promising for condition monitoring and fault diagnosis, but time-domain analysis often lacks effectiveness. Predictive maintenance increasingly relies on artificial intelligence (AI), particularly deep learning (DL), to extract insights from sensor data. DL models can preprocess data, identify features, and diagnose potential failures in critical vessel systems. They can also estimate the remaining useful life (RUL) for maintenance scheduling. Recent studies have advanced techniques like artificial neural networks (ANNs), fuzzy logic, and wavelet transformations ([PERSON] et al., 2018). For instance, [PERSON] et al. (2018) improved ANN classification for induction motor currents, while [PERSON] et al. (2018) developed a DL model for the diagnosis of transformer faults. [PERSON] et al. (2018) created a diagnostic system for three-phase induction motors using CNNs to classify fault types from the current analysis. [PERSON] et al. (2018) found that CNNs trained on scalograms from a continuous wavelet transform outperformed those using manual features. [PERSON] et al. (2018) compared ANNs and support vector machines (SVM) for the diagnosis of bearing faults, with SVMs showing greater efficiency. Despite the great effort, comprehensive lifecycle solutions for ship power systems remain unrealized due to the significant technological challenges in implementing digital twins (DTs). Key issues include the absence of a collaborative framework for subsystem models, non-modular DT architectures that hinder technology integration, and difficulties in managing diverse real-time data. Additionally, deep learning models require extensive training datasets, which are difficult to compile, especially for rare anomalies ([PERSON] et al., 2018). Previous studies have explored the use of autoencoders in various applications, including an LSTM autoencoder for real-time pipeline leak detection using accelerometer signals and a framework for the identification of corrosion in hydropower facilities, achieving over 80% true positive detection ([PERSON] et al., 2018; [PERSON] et al., 2018). Recent studies have shown that autoencoders effectively identify damage in electric motors.