融合模型与数据的故障诊断法，精准识别晶圆搬运机器人断裂皮带和倾斜故障。
Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases
- 用线性滤波器同步估计状态与故障信号，结合支持向量机分类故障类型。
- 在仿真中对突发故障与渐发故障均实现高精度检测与隔离。
- 适合半导体制造中对可靠性要求高的机器人故障诊断场景。
本文提出一种融合模型与数据的故障检测、隔离与估计（FDIE）方法，用于半导体行业中的晶圆搬运机器人。该方法包含：1）一个线性滤波器，从传感与执行数据中同时估计系统状态与故障信号；2）基于支持向量机（SVM）的数据驱动分类器，利用滤波器输出的估计结果进行故障类型检测与隔离。研究针对两类关键故障展开：搬运机器人下臂的皮带断裂（突发故障）与机械臂倾斜（渐发故障）。通过推导故障引发的运动动力学模型，并在真实感仿真案例中验证，结果表明该混合方法在性能上优于纯数据驱动方法。
原文摘要 · Abstract (English)
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.
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