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SPC without Control Limits and Normality Assumption: A New Method
9 references·0 citing·38 second hop
SPC without Control Limits and Normality Assumption: A New Method (2009)SPC without Control Limits and Normality …Real-time pattern recognition in statistical process control: A hybrid neural network/decision tree-based approach (2005)Real-time pattern recognition…An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters (2003)An augmented neural network c…Robustness of the neural network based control chart pattern recognition system to non‐normality (2002)Robustness of the neural netw…Recognition of control chart concurrent patterns using a neural network approach (1999)Recognition of control chart …Efficient control chart pattern recognition through synergistic and distributed artificial neural networks (1999)Efficient control chart patte…An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study (1999)An integrated neural network …A review of neural networks for statistical process control (1998)A review of neural networks f…A neural network approach for the analysis of control chart patterns (1997)A neural network approach for…Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer (1995)Detecting process non-randomn…Identification of change structure in statistical process control (1992)Back-propagation pattern recognizers for control charts: Methodology and performance (1993)X¯ CONTROL CHART PATTERN IDENTIFICATION THROUGH EFFICIENT OFF-LINE NEURAL NETWORK TRAINING (1993)Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach (2008)Shift detection and source identification in multivariate autocorrelated processes (2008)On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model (2011)Statistical Quality Control (1989)X -bar and R control chart interpretation using neural computing (1994)Feature-based control chart pattern recognition (1997)Denoising and feature extraction for control chart pattern recognition in autocorrelated processes (2008)On the Use of the FuzzyARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design (2010)Time-Series Modeling for Statistical Process Control (1988)Statistical process control procedures for correlated observations (1991)Juran's quality handbook (1999)Environmental statistical process control using an augmented neural network classification approach (2005)Applying emerging soft computing approaches to control chart pattern recognition for an SPC–EPC process (2016)Identification of patterns in control charts for processes with statistically correlated noise (2017)Combined Shewhart-CUSUM Quality Control Schemes (1982)Simulation modelling and analysis (1983)Using an MQE chart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes (2008)Determining Statistical Process Control Baseline Periods in Long Historical Data Streams (2010)Multivariate Pattern Recognition in MSPC Using Bayesian Inference (2021)Introduction to Statistical Quality Control (1992)Non‐Normality and the Design of Control Charts for Averages* (1992)Control chart pattern recognition using an optimized neural network and efficient features (2010)The use of neural networks in statistical process control charts (2011)Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines (2014)Economic Control of Quality of Manufactured Product. (1932)Statistical Quality Control (1990)Designing production systems for quality: Research opportunities from an automotive industry perspective (2003)A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme (2012)Control chart pattern recognition using the convolutional neural network (2019)Mean shifts detection and classification in multivariate process: a neural-fuzzy approach (2002)An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts (2008)Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach (2011)A hybrid learning-based model for on-line detection and analysis of control chart patterns (2005)Feature-based recognition of control chart patterns (2006)Recognition of control chart patterns using improved selection of features (2008)
this work references cited by second hop

References

  • 1997A neural network approach for the analysis of control chart patterns169 cited
  • 1998A review of neural networks for statistical process control151 cited
  • 1999Recognition of control chart concurrent patterns using a neural network approach111 cited
  • 1999An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative study67 cited
  • 1995Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer52 cited
  • 2002Robustness of the neural network based control chart pattern recognition system to non‐normality49 cited
  • 2005Real-time pattern recognition in statistical process control: A hybrid neural network/decision tree-based approach33 cited
  • 2003An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters31 cited
  • 1999Efficient control chart pattern recognition through synergistic and distributed artificial neural networks22 cited

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