Fault diagnosis using an enhanced relevance vector machine (RVM) for partially diagnosable multistation assembly processes

  • Bastani K
  • Kong Z
  • Huang W
 et al. 
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Abstract

Dimensional integrity has a significant impact on the quality of the
final products in multistation assembly processes. A large body of
research work in fault diagnosis has been proposed to identify the root
causes of the large dimensional variations on products. These methods
are based on a linear relationship between the dimensional measurements
of the products and the possible process errors, and assume that the
number of measurements is greater than that of process errors. However,
in practice, the number of measurements is often less than that of
process errors due to economical considerations. This brings a
substantial challenge to the fault diagnosis in multistation assembly
processes since the problem becomes solving an underdetermined system.
In order to tackle this challenge, a fault diagnosis methodology is
proposed by integrating the state space model with the enhanced
relevance vector machine (RVM) to identify the process faults through
the sparse estimate of the variance change of the process errors. The
results of case studies demonstrate that the proposed methodology can
identify process faults successfully.

Author-supplied keywords

  • Enhanced relevance vector machine (RVM)
  • fault diagnosis
  • multistation assembly processes
  • partially diagnosable
  • sparse solution

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Authors

  • Kaveh Bastani

  • Zhenyu Kong

  • Wenzhen Huang

  • Xiaoming Huo

  • Yingqing Zhou

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