Direct Wind Turbine Drivetrain Prognosis Approach Using Elman Neural Network

Abstract

Common mechanical failures in wind turbine generators (WTGs) result in unplanned downtime, loose of production and increase the maintenance cost. Statistical studies have shown that failures due to high-speed shaft bearing (HSSB) account for 64% of all drivetrain failures. Consequently, prognostic and health management (PHM) of WTGs aims to estimate the future state of health and predict the reaming useful life (RUL) of HSSB. This paper considers a new data-driven approach based on vibration signals. This approach extracts statistical time-domain features that reflect the behavior of the system and its degradation. Then, the extracted features are evaluated to select the most trendable condition indicators that will be considered as inputs for an Elman neural network (ENN). Moreover, this paper proposes a new ENN architecture for direct RUL estimation of HSSB validated by use of real measured data from a WTG drivetrain. The proposed method reveals accurate estimation capability even with noisy measurements and harsh conditions.