Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915061086289920 |
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| author | van Esch, Tim Ghanipoor, Farhad Murguia, Carlos van de Wouw, Nathan |
| author_facet | van Esch, Tim Ghanipoor, Farhad Murguia, Carlos van de Wouw, Nathan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09114 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases van Esch, Tim Ghanipoor, Farhad Murguia, Carlos van de Wouw, Nathan Robotics Systems and Control 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. |
| title | Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2412.09114 |