Preliminary study of advanced fault detection scheme

Yu Hsuan Shih, Yi Ting Huang, Fan-Tien Cheng

研究成果: Conference contribution

摘要

In high-tech plants, the manufacturing stability and product quality are monitored through periodic sampling. As for those non-sampled workpieces, their quality is commonly monitored by a fault detection and classification (FDC) method. Nevertheless, it may fail to detect out-of-control (OOC) products if their corresponding manufacturing process parameters are all in-spec. In other words, unless those certain defected workpieces are selected for sampling measurements, they may not be detected through simply monitoring all the individual manufacturing process parameters. We have proposed a product quality fault detection scheme (FDS), which utilizes the classification and regression tree (CART) to build a single failure model (FML) for identifying the relationship between process parameters and OOC products. However, all the failure modes (FMs) are contained in the single FML, which makes it difficult to understand the causes of defected products. To remedy this problem, this paper develops an advanced fault detection scheme (AFDS). The AFDS builds the corresponding FM by CART for each individual failure cause and generates a FM manager via support vector machine (SVM) to manage all the FMs. Finally, the dual-phase concept is adopted to run the AFDS for achieving on-line real-time fault detection.

原文English
主出版物標題2013 IEEE International Conference on Robotics and Automation, ICRA 2013
頁面3561-3566
頁數6
DOIs
出版狀態Published - 2013 11月 14
事件2013 IEEE International Conference on Robotics and Automation, ICRA 2013 - Karlsruhe, Germany
持續時間: 2013 5月 62013 5月 10

出版系列

名字Proceedings - IEEE International Conference on Robotics and Automation
ISSN(列印)1050-4729

Other

Other2013 IEEE International Conference on Robotics and Automation, ICRA 2013
國家/地區Germany
城市Karlsruhe
期間13-05-0613-05-10

All Science Journal Classification (ASJC) codes

  • 軟體
  • 控制與系統工程
  • 人工智慧
  • 電氣與電子工程

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