Abstract
Maintenance based on condition and diagnosis technology has a relevant function in Power Electrical System's reliability. This paper proposes the methodological development and computational implementation of a system capable of failure diagnosis on electrical generation assets in order to aid the maintenance crew and the operation at the Coaracy Nunes hydropower plant, located in the Amapa state, in the decision making related with executions of operation and maintenance actions to minimize the incipient issues before they actually occur. The proposed methodology is based in the OSA-CBM (Open System Architecture for Condition-Based Maintenance) model to structure the system’s knowledge base, in addition to implement fuzzy production rules to diagnosis of more complex failures. A case study was realized at the referred plant, showing in the results that the proposed system has a big potential on failure detection. Predictive maintenance and pattern recognition are technological enablers of Industry 4.0.
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