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Statistical Process Control with Intelligence Based on the Deep Learning Model
14 references·12 citing·26 second hop
Statistical Process Control with Intelligence Based on the Deep Learning Model (2019)Statistical Process Control with Intellig…Control chart pattern recognition using the convolutional neural network (2019)Control chart pattern recogni…Control chart pattern recognition using RBF neural network with new training algorithm and practical features (2018)Control chart pattern recogni…Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function (2015)Recognition of control chart …A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme (2012)A survey of control-chart pat…Recognition of control chart patterns using improved selection of features (2008)Recognition of control chart …Improved SPC chart pattern recognition using statistical features (2003)Improved SPC chart pattern re…A neural network approach to characterize pattern parameters in process control charts (1999)A neural network approach to …Feature-based control chart pattern recognition (1997)Feature-based control chart p…A neural network approach for the analysis of control chart patterns (1997)A neural network approach for…Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems (1995)Out-of-control pattern recogn…Control chart pattern recognition using learning vector quantization networks (1994)Control chart pattern recogni…Interpreting Shewhart X Control Charts (1985)Interpreting Shewhart X Contr…The Shewhart Control Chart—Tests for Special Causes (1984)The Shewhart Control Chart—Te…Control Chart Tests Based on Geometric Moving Averages (1959)Control Chart Tests Based on …A review of artificial intelligence impacting statistical process monitoring and future directions (2026)A review of artificial intell…Explainable AI for trustworthy intelligent process monitoring (2025)Explainable AI for trustworth…Statistical Process Monitoring of Artificial Neural Networks (2023)Statistical Process Monitorin…Multivariate Process Control Chart Pattern Classification Using Multi-Channel Deep Convolutional Neural Networks (2023)Multivariate Process Control …Deep learning-based residual control chart for count data (2022)Deep learning-based residual …Control Chart Concurrent Pattern Classification Using Multi-Label Convolutional Neural Networks (2022)Control Chart Concurrent Patt…Concurrent Control Chart Pattern Recognition: A Systematic Review (2022)Concurrent Control Chart Patt…Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns (2021)Fuzzy Heuristics and Decision…End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning (2021)End-to-End Control Chart Patt…Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model (2020)Residual Control Chart for Bi…Pattern Recognition of Different Window Size Control Charts Based on Convolutional Neural Network and Information Fusion (2020)Pattern Recognition of Differ…Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer (2020)Innovative Methods for Small …Design of Experiments (1936)Taguchi Techniques for Quality Engineering (1989)Introduction to Statistical Quality Control (1992)Application of the PSO–SVM model for recognition of control chart patterns (2010)Economic Control of Quality of Manufactured Product. (1932)Measurement errors in statistical process monitoring: A literature review (2016)Understanding the impacts of mobile phone distraction on driving performance: A systematic review (2016)Quality, productivity, and competitive position (1982)The Effect of Sample Size on Estimated Limits forXandXControl Charts (1993)Risks and decisions for conservation and environmental management (2005)Risks and Decisions for Conservation and Environmental Management (2005)Statistical Quality Control (1990)Development of fuzzy process control charts and fuzzy unnatural pattern analyses (2006)A New Automatic Method for Control Chart Patterns Recognition Based on ConvNet and Harris Hawks Meta Heuristic Optimization Algorithm (2019)A Novel Machine Learning Approach Combined with Optimization Models for Eco-efficiency Evaluation (2020)A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation (2021)Control Chart Tests Based on Geometric Moving Averages (2000)Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective (2021)Product backorder prediction using deep neural network on imbalanced data (2021)A review of neural networks for statistical process control (1998)A neural network based model for abnormal pattern recognition of control charts (1999)A control chart pattern recognition system using a statistical correlation coefficient method (2005)A cost-sensitive convolution neural network learning for control chart pattern recognition (2020)The Exponentially Weighted Moving Average (1986)A Multivariate Exponentially Weighted Moving Average Control Chart (1992)Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications (2014)
this work references cited by second hop

References

  • 1959Control Chart Tests Based on Geometric Moving Averages1,544 cited
  • 1984The Shewhart Control Chart—Tests for Special Causes586 cited
  • 2003Improved SPC chart pattern recognition using statistical features175 cited
  • 1997A neural network approach for the analysis of control chart patterns169 cited
  • 1985Interpreting Shewhart X Control Charts147 cited
  • 2012A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme145 cited
  • 1994Control chart pattern recognition using learning vector quantization networks140 cited
  • 1997Feature-based control chart pattern recognition130 cited
  • 2019Control chart pattern recognition using the convolutional neural network114 cited
  • 2018Control chart pattern recognition using RBF neural network with new training algorithm and practical features111 cited
  • 2008Recognition of control chart patterns using improved selection of features97 cited
  • 1995Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems81 cited
  • 1999A neural network approach to characterize pattern parameters in process control charts73 cited
  • 2015Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function68 cited

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Cited by

  • 2020Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer58 cited
  • 2021Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns29 cited
  • 2025Explainable AI for trustworthy intelligent process monitoring26 cited
  • 2022Concurrent Control Chart Pattern Recognition: A Systematic Review26 cited
  • 2022Deep learning-based residual control chart for count data20 cited
  • 2021End-to-End Control Chart Pattern Classification Using a 1D Convolutional Neural Network and Transfer Learning20 cited
  • 2020Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model19 cited
  • 2023Statistical Process Monitoring of Artificial Neural Networks18 cited
  • 2022Control Chart Concurrent Pattern Classification Using Multi-Label Convolutional Neural Networks16 cited
  • 2020Pattern Recognition of Different Window Size Control Charts Based on Convolutional Neural Network and Information Fusion14 cited
  • 2023Multivariate Process Control Chart Pattern Classification Using Multi-Channel Deep Convolutional Neural Networks13 cited
  • 2026A review of artificial intelligence impacting statistical process monitoring and future directions12 cited