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Support vector regression based residual MCUSUM control chart for autocorrelated process
15 references·10 citing·45 second hop
Support vector regression based residual MCUSUM control chart for autocorrelated process (2008)Support vector regression based residual …Artificial neural networks in applying MCUSUM residuals charts for AR(1) processes (2007)Artificial neural networks in…Kernel distance-based robust support vector methods and its application in developing a robust K-chart (2005)Kernel distance-based robust …Effect of Autocorrelation on Performance of the MCUSUM Control Chart (2005)Effect of Autocorrelation on …Multivariate Quality Control Chart for Autocorrelated Processes (2004)Multivariate Quality Control …A kernel-distance-based multivariate control chart using support vector methods (2003)A kernel-distance-based multi…Support vector machines for recognizing shifts in correlated and other manufacturing processes (2002)Support vector machines for r…Model‐based control chart for autoregressive and correlated data (2002)Model‐based control chart for…A CUSUM Scheme for Autocorrelated Observations (2002)A CUSUM Scheme for Autocorrel…Research Issues and Ideas in Statistical Process Control (1999)Research Issues and Ideas in …X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING (1993)X¯ CONTROL CHART PATTERN IDEN…Identification of change structure in statistical process control (1992)Identification of change stru…Effects of autocorrelation on control chart performance (1992)Effects of autocorrelation on…Statistical process control procedures for correlated observations (1991)Statistical process control p…Time-Series Modeling for Statistical Process Control (1988)Time-Series Modeling for Stat…A Note on Multivariate CUSUM Procedures (1987)A Note on Multivariate CUSUM …Multivariate auto‐correlated process control by a residual‐based mixed CUSUM‐EWMA model (2023)Multivariate auto‐correlated …Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing (2022)Machine learning-based contro…Change detection in parametric multivariate dynamic data streams using the ARMAX-GARCH model (2021)Change detection in parametri…Tr(R 2 ) control charts based on kernel density estimation for monitoring multivariate variability process (2019)Tr(R 2 ) control charts based…Multivariate control chart based on PCA mix for variable and attribute quality characteristics (2018)Multivariate control chart ba…Multioutput least square SVR-based multivariate EWMA control chart: The performance evaluation and application (2018)Multioutput least square SVR-…TheT2chart with mixed samples to control bivariate autocorrelated processes (2015)TheT2chart with mixed samples…Data mining model-based control charts for multivariate and autocorrelated processes (2011)Data mining model-based contr…Support Vector Regression control charts for multivariate nonlinear autocorrelated processes (2010)Support Vector Regression con…One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes (2010)One-Class Classification-Base…Out of the Crisis (1982)Introduction to Statistical Quality Control (1992)Response Surface Methodology: Process and Product Optimization Using Designed Experiments (1996)Kollár Ádám Ferenc levelezése (2000)Using Control Charts to Monitor Process and Product Quality Profiles (2004)Effects of Parameter Estimation on Control Chart Properties: A Literature Review (2006)Introduction to Stochastic Control Theory (1970)Time Series Analysis: Forecasting and Control (1977)The Exponentially Weighted Moving Variance (1993)Run-Length Distributions of Special-Cause Control Charts for Correlated Processes (1994)EWMA Control Charts for Monitoring the Mean of Autocorrelated Processes (1999)CONTINUOUS INSPECTION SCHEMES (1954)Quality Control and Industrial Statistics. (1967)Procedures for Reacting to a Change in Distribution (1971)Regression Adjustment for Variables in Multivariate Quality Control (1993)Current research on profile monitoring (2007)Multivariate Statistical Process Control Using LASSO (2009)The Shewhart Control Chart—Tests for Special Causes (1984)Interpreting Shewhart X Control Charts (1985)Back-propagation pattern recognizers for control charts: Methodology and performance (1993)A neural fuzzy control chart for detecting and classifying process mean shifts (1996)Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters (1998)A hybrid learning-based model for on-line detection and analysis of control chart patterns (2005)Multivariate Quality Control (1947)Some Statistical Process Control Methods for Autocorrelated Data (1991)The quality control chart for monitoring multivariate autocorrelated processes (2006)The Effect of Autocorrelation on the Hotelling T2 Control Chart (2014)A nonparametric CUSUM chart for monitoring multivariate serially correlated processes (2020)Self-Starting Cusum Charts for Location and Scale (1987)A neural network approach to characterize pattern parameters in process control charts (1999)On-line control chart pattern detection and discrimination—a neural network approach (1999)Time Series Analysis: Forecasting and Control (1977)Exact Results for Shewhart Control Charts With Supplementary Runs Rules (1987)Statistical Process Monitoring and Feedback Adjustment: A Discussion (1992)A review of multivariate control charts (1995)Introduction to Statistical Quality Control (2001)One-class classification-based control charts for multivariate process monitoring (2009)A Multivariate Sign EWMA Control Chart (2011)Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective (2016)Model-Based and Model-Free Control of Autocorrelated Processes (1995)EVVMA and cusum control charts in the presence of correlation (1997)SPC Procedures for Monitoring Autocorrelated Processes (2007)Mixture control chart patterns recognition using independent component analysis and support vector machine (2011)System Monitoring with Real-Time Contrasts (2012)Support vector machine in statistical process monitoring: a methodological and analytical review (2016)
this work references cited by second hop

References

  • 1988Time-Series Modeling for Statistical Process Control724 cited
  • 1999Research Issues and Ideas in Statistical Process Control631 cited
  • 1991Statistical process control procedures for correlated observations382 cited
  • 1987A Note on Multivariate CUSUM Procedures337 cited
  • 2003A kernel-distance-based multivariate control chart using support vector methods198 cited
  • 1992Effects of autocorrelation on control chart performance178 cited
  • 1993X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING104 cited
  • 1992Identification of change structure in statistical process control99 cited
  • 2004Multivariate Quality Control Chart for Autocorrelated Processes87 cited
  • 2002Support vector machines for recognizing shifts in correlated and other manufacturing processes78 cited
  • 2002Model‐based control chart for autoregressive and correlated data74 cited
  • 2005Effect of Autocorrelation on Performance of the MCUSUM Control Chart66 cited
  • 2002A CUSUM Scheme for Autocorrelated Observations56 cited
  • 2005Kernel distance-based robust support vector methods and its application in developing a robust K-chart53 cited
  • 2007Artificial neural networks in applying MCUSUM residuals charts for AR(1) processes

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47 cited

Cited by

  • 2018Multivariate control chart based on PCA mix for variable and attribute quality characteristics36 cited
  • 2011Data mining model-based control charts for multivariate and autocorrelated processes33 cited
  • 2018Multioutput least square SVR-based multivariate EWMA control chart: The performance evaluation and application26 cited
  • 2010Support Vector Regression control charts for multivariate nonlinear autocorrelated processes24 cited
  • 2019Tr(R 2 ) control charts based on kernel density estimation for monitoring multivariate variability process23 cited
  • 2015TheT2chart with mixed samples to control bivariate autocorrelated processes23 cited
  • 2010One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes22 cited
  • 2022Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing18 cited
  • 2023Multivariate auto‐correlated process control by a residual‐based mixed CUSUM‐EWMA model15 cited
  • 2021Change detection in parametric multivariate dynamic data streams using the ARMAX-GARCH model12 cited