Planetary gearbox is one of the most important components of rotating machinery and plays a key role in modern industry. Due to the complex physical structures and harsh working conditions, planetary gearbox often suffers from different fault types, so it is of vital importance to investigate its fault diagnosis task. In this paper, a novel feature selection strategy is proposed to improve the multiclass support vector data description (SVDD) algorithm for planetary gearbox fault diagnosis. First, a novel feature selection method based on the cosine similarity measure in kernel space of Gaussian radial basis function (GRBF) is presented, so as to determine features that are sensitive to faults. Then, based on the selected features, an improved multiclass SVDD algorithm is developed to classify multiple classes of planetary gear faults, thus completing the fault diagnosis task. Finally, the effectiveness and advantage of the proposed method are demonstrated via experiments using wind turbine drivetrain diagnostics simulator (WTDDS), with comparison to several traditional methods.

行星齿轮箱是旋转机械最重要的部件之一，在现代工业中发挥着关键作用。由于复杂的物理结构和恶劣的工作条件，行星齿轮箱经常出现不同的故障类型，因此研究其故障诊断任务至关重要。在本文中，提出了一种新的特征选择策略，以改进用于行星齿轮箱故障诊断的多类支持向量数据描述（SVDD）算法。首先，提出了一种新的基于高斯径向基函数(GRBF)核空间余弦相似度度量的特征选择方法，以确定对故障敏感的特征。然后，基于选择的特征，开发了一种改进的多类 SVDD 算法来对多类行星齿轮故障进行分类，从而完成故障诊断任务。最后，通过使用风力涡轮机传动系统诊断模拟器 (WTDDS) 的实验，与几种传统方法进行比较，证明了所提出方法的有效性和优势。