Planetary gear reducer is widely used in industrial automation, and its performance highly affects the equipment reliability. The backlash and stiffness may cause the performance decline of planetary, hence the vibration, temperature, current and other signals are applied in planetary condition monitoring. The purpose of this paper is to develop a practical and effective method based on motor current signal analysis (MCSA) to identify backlash faults of planetary gear reducers. The sensitivity weight ratio (SWR) is proposed to optimize the introduced fisher discriminant analysis (FDA) algorithm, which is used to extract and screen the current signal characteristics of the servo motor. The motor is connected to the reducer, so the changes in the operating conditions of the planetary gears can be observed in the motor current. Compared with the traditional detection method of equipment health status, the Hall current sensor is a non-invasive method with lower cost and easy installation. Besides, the support vector machine (SVM) classifier and some published methods are utilized to classify the backlash of the planetary gear. Finally, experimental tests were carried out under different backlashes and loads to verify the effectiveness of the method.

行星齿轮减速器广泛应用于工业自动化领域，其性能严重影响设备的可靠性。齿隙和刚度可能会导致行星齿轮的性能下降，因此，振动，温度，电流和其他信号会应用到行星齿轮状态监测中。本文的目的是开发一种基于电动机电流信号分析（MCSA）的实用有效的方法来识别行星齿轮减速器的齿隙故障。提出了灵敏度权重比（SWR）来优化引入的Fisher判别分析（FDA）算法，该算法用于提取和筛选伺服电机的当前信号特征。电动机连接到减速器，因此可以在电动机电流中观察到行星齿轮的运行条件的变化。与传统的设备健康状态检测方法相比，霍尔电流传感器是一种非侵入性方法，成本较低且易于安装。此外，利用支持向量机（SVM）分类器和一些公开的方法对行星齿轮的齿隙进行分类。最后，在不同的间隙和载荷下进行了实验测试，以验证该方法的有效性。