# SOPRENE: Assessment of the Spanish Armada's Predictive Maintenance Tool for Naval Assets
## 6 Conclusions  
We have presented a scalable, robust and flexible predictive maintenance architecture for big and complex systems. It follows a modular design based on three independent blocks that can be modified, replaced or customized without altering the flow. It consists of the following: (1) a data processing module, (2) a data training module and (3) an operational module. The virtues and limitations of this architecture have been verified and validated by the status monitoring of diverse diesel engine equipment (diesel engines for propulsion vessel's propulsion and diesel generators) in two different ship classes of the Spanish Armada. In this sense, the data prediction, the detection of anomalies and the diagnosis of failure modes were carried out in two assets from a total of 28 tested in different vessels. As shown along the paper, these predictions were correctly adjusted to what happened in different time horizons' predictions. In the same manner, correct diagnoses of the probabilities of failure modes were carried out prior to the appearance of alarms on the monitored systems. In brief, the percentages of detectability were high enough to assume that this technological demonstrator is a great advancement in supporting human decisions of the human teams that are in charge of Spanish Armada's units sustainment. Finally, the solution developed is a comprehensive system that allows the three predictive maintenance tasks (prognosis, anomaly detection and diagnosis) to be carried out in a reasonable time due to its distributed implementation, which allows working on large data sets (Big Data scenarios). In addition, it is valid for any ship in the fleet with similar characteristics and, due to its modularity, it can be easily adapted to new environments. To the best of our knowledge, this is the only complete predictive maintenance software for Big Data that has been used successfully in the naval sector. After this research study, we have verified that it is possible to define a general architecture that allows scalable predictive maintenance in a fleet of ships. Several lines of work remain open. Given the flexibility of SOPRENE's and the Spanish Armada's needs, we plan to extend the platform to apply PdM to new equipment (fire extinguishers, naval electric engines, axes, etc.) and ships and, in this manner, reduce the maintenance cost of the fleet while increasing its operational capacity. One of the system limitations is that it has not been designed to operate in real-time and depends on complex offline processing in centralized data centers. To overcome this problem, we plan to extend SOPRENE by embedding it into the Spanish Armada's ships operating in real-time with limited computational resources. Conceptualization, D.F.-B., O.F.-R. and D.S. ; Data curation, D.N.-P.; Formal analysis, D.F.-B., O.F.-R., D.N.-P. and D.S. ; Funding acquisition, F.L.-L.; Investigation, D.F.-B., O.F.-R. and D.N.-P.; Methodology, D.F.-B., O.F.-R. and D.S. ; Project administration, F.L.-L. and D.S. ; Resources, F.L.-L., D.F.-B. and O.F.-R.; Software, D.N.-P.; Supervision, D.S. ; Validation, F.L.-L.; Writing--original draft, D.F.-B., O.F.-R., F.L.-L., D.N.-P., M.D.R.-M. and D.S. All authors have read and agreed to the published version of the manuscript. Figure 10: Normalized comparison of the vessel alarms (in blue) with the autoencoder’s joint diagnostics for all failure modes. Data comes from a F-100 class frigate. Y-axis represents the confidence given by the autoencoder. X-axis represents normalized data coming from the vessel. **Funding:** This research was funded by DGAM-PLATIN (General Directorate of Armament and Material--Spanish Ministry of Defence) with project funding number '2018/1003218001400'. **Data Availability Statement:** Data sharing not applicable (Confidential data). **Acknowledgments:** This paper reports part of the results obtained within the 'SOPRENE' research and development project funded by DGAM-PLATIN (General Directorate of Armament and Material--Spanish Ministry of Defence).