Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines

Highlights: • A novel approach based on artificial neural networks to reliability centred maintenance. • The methodology is employed for false alarm detection and prioritization. • The approach is applied to a real dataset from a SCADA together with a vibration CM. • The results are validated by confusion matrices, studding real alarms with the estimations provided by the approach. • The method provides accuracy results (over 90%). • A novelty is to use a two real dataset from a wind turbine to create a redundant response to detect false alarms. -- Abstract: Operations and maintenance tasks are critical to the reliability of a wind turbine. The state-of-the-art demonstrates the effectiveness of reliability centred maintenance, but there are no research studies that consider false alarms to reliability of the wind turbines. This paper presents a novel approach based on artificial neural networks to reliability centred maintenance. The methodology is employed for false alarm detection and prioritization, training the artificial neural networks over the time to increase the system reliability. The approach is applied to a real dataset from a supervisory control and data acquisition system together with a vibration monitoring system of a wind turbine. The results accuracy is done by confusion matrices, studding real alarms with the estimations provided by the approach, and the results are validated with real false alarms and compared by the results given by a fuzzy logic model. The method provides accuracy results (over 90%). A novelty is to use a two real dataset from a wind turbine to create a redundant response to detect false alarms by artificial neural networks.