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Support vector machine in statistical process monitoring: a methodological and analytical review

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References

2009Statistical quality control : a modern introduction572 cited
2003A kernel-distance-based multivariate control chart using support vector methods198 cited
2010Application of the PSO–SVM model for recognition of control chart patterns148 cited
2009One-class classification-based control charts for multivariate process monitoring122 cited
2005A hybrid learning-based model for on-line detection and analysis of control chart patterns103 cited
2008Recognition of control chart patterns using improved selection of features97 cited
2007Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines90 cited
2006Feature-based recognition of control chart patterns89 cited
2013Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines82 cited
2002Support vector machines for recognizing shifts in correlated and other manufacturing processes78 cited
2011Mixture control chart patterns recognition using independent component analysis and support vector machine77 cited
2007A hybrid system for SPC concurrent pattern recognition74 cited

Cited by

2021Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective52 cited
2021

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Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine

29 cited

2024Adaptive EWMA control chart by using support vector regression28 cited

2025Explainable AI for trustworthy intelligent process monitoring26 cited

2024Monitoring bivariate autocorrelated process using a deep learning-based control chart: A case study on the car manufacturing industry20 cited

2022Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing18 cited

2024Machine learning based parameter-free adaptive EWMA control chart to monitor process dispersion16 cited

2022An SVM-GA based monitoring system for pattern recognition of autocorrelated processes16 cited

2019Efficient mixture control chart pattern recognition using adaptive RBF neural network14 cited

2021A robust meta‐method for interpreting the out‐of‐control signal of multivariate control charts using artificial neural networks14 cited