Preliminary study of advanced fault detection scheme

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Robotics and Automation, ICRA 2013
Number of pages6
Publication statusPublished - 2013 Nov 14
Event2013 IEEE International Conference on Robotics and Automation, ICRA 2013 - Karlsruhe, Germany
Duration: 2013 May 62013 May 10

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Other2013 IEEE International Conference on Robotics and Automation, ICRA 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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