A Data-Driven Diagnostic System Utilizing Manufacturing Data Mining and Analytics

Weihong Guo, Shenghan Guo, Hui Wang, Xiao Yu, Annette Januszczak, Saumuy Suriano

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The proposed method extends the basic attributes control charts with the following key elements: optimal feature subset selection considering multiple features and correlation structure, balancing the type I and type II errors in decision making, on-line process monitoring using adaptive modeling with control charts, and diagnostic performance assessment using shift and trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production. An example of the analysis at one Ford production plant is provided to demonstrate the implementation of this methodology.

Original languageEnglish (US)
Pages (from-to)282-292
Number of pages11
JournalSAE International Journal of Materials and Manufacturing
Issue number3
StatePublished - Mar 28 2017
Externally publishedYes


  • Bayesian inference
  • Feature selection
  • Markov chain Monte Carlo (MCMC) algorithm
  • attributes control chart
  • manufacturing system
  • process monitoring

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


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