【摘要】 针对小样本风力发电机齿轮箱给故障检测模型带来的过拟合和泛化能力差等问题，提出了基于强化学习马田系统(reinforcement learning Mahalanobis-Taguchi system, RLMTS)的故障检测模型。首先将经过正交表和信噪比筛选后的特征作为初始马氏空间，其次利用强化学习和给定规则对其进行探索和优化，最后采用蚁狮优化器对传统马田系统的阈值确定方式进行改进。实验结果显示，RLMTS适用于不同小样本量下的故障检测，且与17种比较方法相比，RLMTS诊断性能更优，鲁棒性更强，适用性更广，更适用于小样本风力发电机齿轮箱的故障检测。RLMTS有利于提高齿轮箱运行的可靠性、高效性和安全性，同时降低维护成本，进而保障风力发电的稳定性和高效益。更多还原

【Abstract】 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.更多还原