## 5 Conclusions  
In the current work, an effort was made to investigate how deep learning approaches can be employed to exploit a heterogeneous source of information coming from data collected onboard. The model developed was implemented using real case data for the detection and real-time condition monitoring of the primary systems. The work aimed to develop a framework for decision support on maintenance activities in short-, medium-, and long-term time horizons. Furthermore, an in-depth investigation of the effect of multiple variables on the performance of the models was presented. In this context, the parametric investigation was undertaken to attempt to determine how accurately different levels of degradation would be detected, together with the effect of the input time interval on the proposed network's efficacy. Additionally, due to the absence of data reflecting degraded operational profiles of the vessel under study, artificial damage had to be effectuated, trying to emulate different realistic degradation cases for each methodology to detect. Concluding, it was shown that 1D-CNN models can infer the dynamic properties of the vessel components' decay and status on different time horizons. It was concluded that the short-term FD mechanism benefitted from the shrinkage of the input window, profoundly increasing yielded accuracy in the predictions on the test set. Moreover, regarding the degradation variable models, results indicated that the proposed methodology was more potent as the degradation degree increased. However, it could be claimed that all levels examined were adequately detected, indicating the efficiency of the proposed methodology contributing to the timely failure detection; in contrast, the benchmark ML classifiers presented failed to identify adequately defective patterns for small degenerations. Lastly, it was also evinced that the proposed methodology not only accurately identifies the failure proneness in the system of the main engine, but also manages to localize the occurrence of the trigger event with great accuracy. Moving forward, in our future work we will perform a further parametric investigation of the CNN models, to evaluate the efficiency of the results with different damage severity and data sample overlap percentage. Additionally, a parametric study will be investigated, where multiple cylinders are defective in the same instance. Additionally, the next step in this direction will be to assess the efficacy of our models by utilizing real samples that have been pre-established and pre-labeled as defective, since the applicability of the proposed networks has been evinced successfully. Finally, examination of the applicability of the results of a trained model based on the data of a particular vessel to evaluate the condition of another ship of similar type or even considerably dissimilar is amongst our short-term plans. Conceptualization, C.C.S. and F.G.; Data curation, P.T. ; Formal analysis, P.T., C.C.S., F.G. and S.F. Funding acquisition, C.C.S. ; Investigation, C.C.S. ; Methodology, P.T., C.C.S. and S.F. ; Project administration, C.C.S., F.G. and S.F. ; Resources, S.F. ; Software, C.C.S. ; Validation, P.T. and F.G.; Writing--original draft, P.T. and C.C.S. ; Writing--review & editing, C.C.S., F.G. and S.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.