DIANA: Creación de un modelo de normalidad multi -parametral para detección de malfuncionamientos en equipos de plataforma naval

Abstract

An artificial intelligence system needs to have a complete and reliable database that facilitates the identification and diagnosis of a failure when the indications that characterize it are activated: its sensorized parameters. For this, a Failure Modes, Effects and Criticality Analysis for Artificial Intelligence (FMECA-AI) or its translation: "Analysis of Failure Modes, Effects and Criticality for Artificial Intelligence" of the studied system is carried out. In this way, not only the list of failures is available, also the effects it produces, the criticality of the consequences and how they are measured, and then implanted in a predictive system based on artificial intelligence. For this it is necessary to cross the effects produced by each failure with the indicators that facilitate the monitored signals in order to connect the detection subsystem with the fault database. This allows us to obtain a diagnosis of what is the failure or the possible failures that are occurring when anomalous variation is detected in the sensorized data. The procedure used is based on the existing one used to create a fault database called FMECA. This is included in the so-called Reability Center Maintenance (RCM) "maintenance based on reliability", due it is a successful systematic and efficient procedure verified in aeronautics, defense and in the industry in general.