ウェハハンドリングロボットのハイブリッドモデル・データ故障診断：傾きとベルト破断の事例
Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases
ウェハハンドリングロボットの故障検出・特定・推定のため、線形フィルタとSVMを組み合わせたハイブリッド手法を提案し、ベルト破断やアームの傾きに対して純粋なデータ駆動法より優れることを示した。
著者: Tim van Esch, Farhad Ghanipoor, Carlos Murguia, Nathan van de Wouw
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
