Wind turbine gearbox condition monitoring through the sequential analysis of industrial SCADA and vibration data

The operation & maintenance expenditure for a wind farm project can reach the impressive share of 30% of the total costs. This matter of fact motivates the need for optimal operation & maintenance, which is estimated to provide up to a 10% of energy production improvement. Such potential benefit can only be achieved through an efficient condition monitoring and predictive maintenance strategy. Based on these motivations, this paper presents a real-world case study in which standard diagnostic techniques failed to detect severe faults in the planetary stage of a wind turbine gearbox in time to prevent prolonged downtime. To address this issue, a measurement data processing and fusion algorithm is developed. The approach is capable of leveraging all the information from different data sources (with low to high time resolution) using different machine and deep learning algorithms, connected between them in cascade. This enables the detection of the fault some weeks in advance, compared to the commonly used methods and with lower-level processing of industrial operational data. A qualifying feature of the proposed workflow is that it enables the identification of the faulty component, which is a well known critical point in real-world applications.