Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases

TL;DR

This paper introduces a hybrid fault diagnosis scheme combining model-based filtering and data-driven classification to detect and isolate faults in wafer handler robots, demonstrating improved accuracy over purely data-driven methods.

Contribution

The paper presents a novel hybrid fault detection, isolation, and estimation approach that integrates linear filtering with SVM classification for wafer handler robots.

Findings

Effective detection of broken-belt faults in wafer robots

Accurate tilt fault identification in robot arms

Superior performance compared to purely data-driven methods

Abstract

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.