Mean shifts detection and classification in multivariate process: A neural-fuzzy approach

Tai Yue Wang, Long Hui Chen

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)


For monitoring multivariate quality control process, traditional multivariate control charts have been proposed to detect mean shifts. However, a persistent problem is that such charts are unable to provide any shift-related information when mean shifts occur in the process. In fact, the immediate classification of the magnitude of mean shifts can greatly narrow down the set of possible assignable causes, hence facilitating quick analysis and corrective action by the technician before many nonconforming units are manufactured. In this paper, we propose a neural-fuzzy model for detecting mean shifts and classifying their magnitude in multivariate process. This model is divided into training and classifying modules. In the training module, a neural network (NN) model is trained to detect various mean shifts for multivariate process. Then, in the classifying module, the outputs of NN are classified into various decision intervals by using a fuzzy classifier and an additional two-point-in-an-interval decision rule to determine shift status. An example is presented to illustrate the application of the proposed model. Simulation results show that it outperforms the multivariate T2 control chart in terms of out-of-control average run length under fixed type I error. In addition, the correct classification percentages are also studied and the general guidelines are given for the proper use of the proposed model.

Original languageEnglish
Pages (from-to)211-221
Number of pages11
JournalJournal of Intelligent Manufacturing
Issue number3
Publication statusPublished - 2002 Jun

All Science Journal Classification (ASJC) codes

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence


Dive into the research topics of 'Mean shifts detection and classification in multivariate process: A neural-fuzzy approach'. Together they form a unique fingerprint.

Cite this