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

Tai Yue Wang, Long Hui Chen

研究成果: Article同行評審

72 引文 斯高帕斯(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.

頁(從 - 到)211-221
期刊Journal of Intelligent Manufacturing
出版狀態Published - 2002 6月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 工業與製造工程
  • 人工智慧


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