## 1 Introduction
The use of ML for fault detection and isolation on gas turbines (prevalent in naval applications) has been extensively studied [23]; rolling element bearings are among the most well-studied components in the literature [24], asynchronous [25], and synchronous [26] motors, and batteries [27], have seen successful applications of ML. ML applications will play a key role in the maritime sector during the coming decades for the estimation and degradation modeling of assets [28; 29]. Working toward the same direction, in our previous work we investigated how different Deep-Learning (DL) models could provide enhanced information for the support of decision-making [30] within the maritime industry, based on deep learning algorithms. It was shown that the method could accurately predict issues that are caused by long-term degradation (e.g., hull performance) but its effectiveness when instantaneous faults occurred was limited. In this study, our primary focus is to transcend our previous work and progress one step further in the preventive maintenance spectrum implementing condition-based monitoring aspiring to detect failure proneness. It is aspired to develop a CBM scheme to monitor the state of the vessel under study and identify suspicious patterns that indicate failure proneness. More specifically, the signals of the temperature of the main engine's cylinders are monitored, trying to identify patterns in the signals that signify proneness to failure of the monitored part of the machinery. The type of degradation aspired to detect is either caused by trigger events, such as impacts, or by fracture propagation. These types of degradation are demonstrated in the monitored signal by the inflated jittering in the signal. Figure 1 demonstrates the type of deficiency we aspire to detect with our methodology, illustrating the occurrence of a trigger, even to the actual signal collected from the Diesel Generator (D/G) Lube Oil (LO) pressure over approximately 2 days, as the impairment manifests itself through the inflated volatility due to a crack in the crankshaft. The blue circle in the figure shows the commencement of damage. According to the vessel operator, the fault was not visible by the instruments onboard and the root cause was found only after costly unscheduled maintenance. The proposed implementation employs a unidimensional (1D) Convolutional Neural Network (CNN) that monitors of the condition of the main engine, which inspects the signal of the temperature of the main engine's cylinders (Figure 2). This CBM system facilitates the implementation of a short-term approach, as the time series can be divided into smaller batches without affecting adversely the performance of the model. In this instance, we attempt to classify the signal obtained from the exhaust gas temperature of the six cylinders of the ship's main engine, detecting divergence from the healthy state, caused by degradation and wear, assuming that in the context of our study case, the cases examined refer to occasions with only up to one cylinder being degraded. This form of deficiency is typically expressed as increased jitter of the monitored signal. The networks receive as inputs time series batches of the six signals of the exhaust gas temperature of the six cylinders representing the same time interval. The output is the probabilities of the validity for each possible state of the seven distinct states. Let the available label range be 0-6, then the label 0 signifies a well-functioning vessel, or equivalently, the label i expresses that the respective cylinder i is defective (i.e., 1-6). Therefore, it is aimed to not only identify the operational condition of the ship through the classification of time series batches derived from the dataset, but also, in case of deficiency, to explicitly localize the damage. In this regard, the prominence of CNNs in pattern recognition is examined through their capacity to extract and represent features at high levels of abstraction.