An integrated monitoring scheme for wind turbine main bearing using acoustic emission

The condition monitoring of the main bearing (MB) plays a crucial role in the maintenance of wind turbines (WT), especially for direct-drive wind turbines (DDWT). However, due to the harsh operating environment and ultra-low rotating speed, the condition monitoring of the MB is still a challenging issue. In this study, an integrated monitoring scheme using acoustic emission (AE) is proposed for incipient fault detection and localization of MB. First, an rotating speed estimation approach using high-frequency envelope autocorrelation (HFEA) is developed to recover the accurate operating speed of MB. On this basis, the adapted spectral coherence (ASC) is explored to identify faulty sources buried under multiple disturbances. Finally, an effective damage localization model is further constructed to improve maintenance efficiency in practical applications. The performance of the proposed methodology is evaluated through two engineering cases with natural damages. Compared with state-of-the-art approaches, the proposed method can not only effectively detect the incipient damage of the MB, but also accurately determine the damage location. With this scheme, the inspection efficiency can be improved, thus it may provide a promising tool for the health management of WT.