Indra, Partners Present Best of 140 Papers
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A research paper on Artificial Intelligence (AI) developed by Indra, the Spanish Navy’s Data Analysis and Monitoring Center (CESADAR), and the University of A Coruña was highlighted as the best of all of the articles presented in the VIII National Congress for R&D in Defence & Security (DESEi+d) event.
Of the 140 papers accepted, this one aroused the most interest among members of the Congress Committee for its level of innovation, and it decided to publish it to share it with the scientific community.
Under the title “Predictive maintenance of vessel engines through automatic learning”, the document analyzes the use of new AI techniques to detect engine malfunctions before they occur. The main author of the paper is David Novoa, a researcher at the University of A Coruña, together with his Master’s degree supervisors, Carlos Eiras and Oscar Fontenla, with support from CESADAR’s Official Technician, Lieutenant Francisco Lamas and Dr. David Sanz from Indra. The paper is derived from the R&D+i SOPRENE project, funded by the Directorate-General for Armament and Material - Sub-Directorate-General for Planning, Technology and Innovation.
What are some current developments from the Royal Navy's synthetic training? Read Current and Future Operational Training in the Royal Navy to find out.
Within the framework of the SOPRENE project, a technological demonstrator that enables predictive maintenance in vessels was successfully tested. The solution combines deep learning, predictive and diagnostic algorithms and uses ‘unsupervised’ intelligence.
Unlike supervised AI, it uses a strategy focused on teaching machines to ‘learn how to detect and solve problems’, without needing to have prior knowledge. This makes it easier to use with platforms, systems or equipment that are very different from one another and enables unrecorded errors and ones that have never occurred before to be detected, something in which it differs from a conventional AI system.
Similarly, when combined with the predictive algorithms which have also been developed, it is able to detect the most serious faults in advance, including those that have never occurred before and which, if they were left unresolved, could put the crew at risk.
The technology offers numerous applications. In the military field, it can be used to maintain the equipment and platforms of any of the three armed forces, while in the civilian field, it can improve the maintenance of all types of industrial equipment. For example, maintenance costs can account for up to 60% of total production costs in the metallurgical industry.
