To address issues such as overfitting and poor generalization caused by small-sample data in wind turbine gearbox fault detection

this paper proposes a fault detection model based on the reinforcement learning Mahalanobis-Taguchi system (RLMTS). First

features filtered by orthogonal array and signal-to-noise ratio analysis are used to construct the initial Mahalanobis space. Then

reinforcement learning and predefined rules are used to explore and optimize this space. Finally

an antlion optimizer is employed to improve the threshold determination of the traditional Mahalanobis Taguchi system. Experimental results show that RLMTS is effective for fault detection in different small-sample scenarios. Compared with 17 other methods

RLMTS has better diagnostic performance

greater robustness

and broader applicability

making it particularly suitable for small-sample wind turbine gearbox fault detection. It is conducive to improving the reliability

efficiency and safety of the gearbox operation

while also reducing maintenance cost and ensuring stable and high efficiency wind power generation.