Nonstationary fault detection and diagnosis for multimode processes

  • Liu J
  • Chen D
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Fault isolation based on data-driven approaches usually assume the
abnormal event data will be formed into a new operating region,
measuring the differences between normal and faulty states to identify
the faulty variables. In practice, operators intervene in processes when
they are aware of abnormalities occurring. The process behavior is
nonstationary, whereas the operators are trying to bring it back to
normal states. Therefore, the faulty variables have to be located in the
first place when the process leaves its normal operating regions. For an
industrial process, multiple normal operations are common. On the basis
of the assumption that the operating data follow a Gaussian distribution
within an operating region, the Gaussian mixture model is employed to
extract a series of operating modes from the historical process data.
The local statistic T 2 and its normalized contribution chart have been
derived for detecting abnormalities early and isolating faulty variables
in this article. (C) 2009 American Institute of Chemical Engineers AIChE
J, 56: 207-219, 2010

Author-supplied keywords

  • Contribution charts
  • Gaussian mixture model
  • Kernel density estimation
  • Principal component analysis
  • Process monitoring

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  • Jialin Liu

  • Ding Sou Chen

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