This work proposes a hybrid model- and data-based scheme for fault detection, isolation, and estimation (FDIE) for a class of wafer handler (WH) robots. The proposed hybrid scheme consists of: 1) a linear filter that simultaneously estimates system states and fault-induced signals from sensing and actuation data; and 2) a data-driven classifier, in the form of a support vector machine (SVM), that detects and isolates the fault type using estimates generated by the filter. We demonstrate the effectiveness of the scheme for two critical fault types for WH robots used in the semiconductor industry: broken-belt in the lower arm of the WH robot (an abrupt fault) and tilt in the robot arms (an incipient fault). We derive explicit models of the robot motion dynamics induced by these faults and test the diagnostics scheme in a realistic simulation-based case study. These case study results demonstrate that the proposed hybrid FDIE scheme achieves superior performance compared to purely data-driven methods.

翻译：本研究提出了一种混合模型与数据驱动的故障检测、隔离与估计方案，适用于一类晶圆传输机器人。所提出的混合方案包含：1）一个线性滤波器，能够同时从传感与驱动数据中估计系统状态及故障诱导信号；2）一个以支持向量机形式实现的数据驱动分类器，利用滤波器生成的估计值进行故障类型的检测与隔离。我们针对半导体工业中晶圆传输机器人两种关键故障类型验证了该方案的有效性：机器人下臂断带（突发故障）与机器人手臂倾斜（渐发故障）。我们推导了由这些故障引起的机器人运动动力学的显式模型，并通过基于仿真的真实案例研究测试了该诊断方案。案例研究结果表明，与纯数据驱动方法相比，所提出的混合故障检测、隔离与估计方案具有更优越的性能。