Identification of change structure in statistical process control

Guo, Yongqing; Dooley, Kevin

doi:10.1080/00207549208948112

Cited by 91 publications

(20 citation statements)

References 7 publications

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“…Cheng (1995) later trained a multi-layer perceptron (MLP) network to solve the same problem. Guo and Dooley (1992) developed network models to identify positive changes in mean or variance. Hwarng and Hubele (1993) classified six unnatural patterns-trend (up or down), cycle, stratification, systematic, mixture and sudden shifts (up or down).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks to classify mean shifts from multivariate χ2 chart signals

Chen

Wang

2004

Computers & Industrial Engineering

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Section: Introductionmentioning

confidence: 99%

Artificial neural networks to classify mean shifts from multivariate χ2 chart signals

Chen

Wang

2004

Computers & Industrial Engineering

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“…The approaches adopted range from statistical methods to fuzzy logic and neural networks. Guo and Dooley (1992) made use of CUSUM and MOSUM statistics along with other data lo train a back-propagation neural network to identify shifts in process mean andor variability. Results were found to be comparable to those produced by Bayesian discriminant functions.…”

Section: H B Hwarngmentioning

confidence: 99%

Multilayer perceptions for detecting cyclic data on control charts

Hwarng

1995

International Journal of Production Research

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“…They introduced an enhanced quality evaluation system which can detect and classify changes of continuous manufacturing processes using time series models, Cusum charts, chi-squared tests, autocorrelation charts, and a role-based classifier. In addition, it was stated that "Experience shows that many SPC attempts fail to produce meaningful results because of the lack of diagnostic support for the effort" (Guo and Dooley 1992).…”

Section: Introductionmentioning

confidence: 99%

Monitoring and classification of dimensional faults for automotive body assembly

Roan

1995

Int J Flex Manuf Syst

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Abstract. Sudden process changes occurring during automobile body assembly processes will influence the downstream assembly process and the functionality and final appearance of the vehicle. Furthermore, these faults could result in a decreased production rate and an increase in the cost if sudden process changes are so serious that the production line has to be stopped for investigation. Thus, sudden process changes should be detected and eliminated as soon as possible to prevent defective products from being produced and to reduce the cost of repairs/ reworks. A monitoring algorithm is developed to detect, classify, and group process changes by analyzing the dimensional data of ear bodies. The results of this monitoring algorithm can help diagnose the root causes of variation according to the locations of measurement points, body structure, assembly sequence, and tooling layout. Measurement data obtained from an optical coordinate measuring machine (OCMM) are used to demonstrate the monitoring ~echnique.

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Identification of change structure in statistical process control

Cited by 91 publications

References 7 publications

Artificial neural networks to classify mean shifts from multivariate χ2 chart signals

Artificial neural networks to classify mean shifts from multivariate χ2 chart signals

Multilayer perceptions for detecting cyclic data on control charts

Monitoring and classification of dimensional faults for automotive body assembly

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