Wind turbine main bearing fault detection via shaft speed signal analysis under constant load

Early detection of bearing faults is very critical since they cannot be compensated using analytical methods, such as reconfigurable control. From the surveys of current conditions monitoring (CM) systems, there is a clear tendency towards vibration monitoring of wind turbines (WTs). It is likely that this tendency will continue, however it would be reasonable to assume that other CMs and diagnosis techniques will be incorporated into existing systems, with major innovation in terms of developing signal processing techniques. In particular, the industry is already noting the importance of operational parameters such as load and speed and so techniques may begin to adapt further to the WT environment leading to more reliable CM systems, diagnostics and alarm signals. Therefore, this paper presents a Wind Turbine Main Bearing (WTMB) fault detection method via speed signal analysis under constant load providing a benefit in terms of cost, and space. Since process history-based bearing fault detection has considerable advantages in terms of simplicity and implementation, the presented WTMB fault detection method base on Absolute Value Principal Component Analysis (AVPCA) technique. A set of bearing faults with outer-race, inner-race, and ball/roller failure are evaluated to demonstrate the performance and effectiveness of the proposed method.