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An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts

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References

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1995A review of multivariate control charts703 cited
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1947Multivariate Quality Control549 cited
1990Comparisons of MultivariateCUSUMCharts520 cited
1995Decomposition of T 2 for Multivariate Control Chart Interpretation397 cited
1991Multivariate Quality Control Based on Regression-Adjusted Variables378 cited
1993Regression Adjustment for Variables in Multivariate Quality Control355 cited
1985Multivariate quality control296 cited
1997A Practical Approach for Interpreting Multivariate T2 Control Chart Signals252 cited
1989Statistical Methods for Quality Improvement250 cited
1997A neural network approach for the analysis of control chart patterns

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169 cited

Cited by

2020Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review72 cited
2011Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques59 cited
2016A distance-based control chart for monitoring multivariate processes using support vector machines55 cited
2012On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines53 cited
2021Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective52 cited
2016A robust self-starting spatial rank multivariate EWMA chart based on forward variable selection52 cited
2009A hierarchy of adaptive control charts46 cited
2014Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines45 cited
2010An integrating approach to root cause analysis of a bivariate mean vector with a linear trend disturbance45 cited
2013Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble42 cited
2015A general framework for monitoring complex processes with both in-control and out-of-control information41 cited
2011On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model40 cited
2012A multivariate synthetic double sampling T2 control chart36 cited
2012On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes33 cited
2013Monitoring and diagnosing of mean shifts in multivariate manufacturing processes using two-level selective ensemble of learning vector quantization neural networks26 cited
2019One‐sided control chart based on support vector machines with differential evolution algorithm26 cited