Back-propagation pattern recognizers for X¯ control charts: methodology and performance
Computers and Industrial Engineering
Efficient training of recurrent neural network with time delays
Neural Networks
Computers and Industrial Engineering
A statistical control chart for stationary process data
Technometrics
A neural network based model for abnormal pattern recognition of control charts
Computers and Industrial Engineering
A neural network-based procedure for the monitoring of exponential mean
Computers and Industrial Engineering
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Modeling and optimization of stencil printing operations: A comparison study
Computers and Industrial Engineering
Approximate distribution of demerit statistic-A bounding approach
Computational Statistics & Data Analysis
Simultaneous process mean and variance monitoring using artificial neural networks
Computers and Industrial Engineering
Fault diagnosis in assembly processes based on engineering-driven rules and PSOSAEN algorithm
Computers and Industrial Engineering
Neural networks for detecting cyclic behavior in autocorrelated process
Computers and Industrial Engineering
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With the growing of automation in manufacturing, process quality characteristics are being measured at higher rates and data are more likely to be autocorrelated. A widely used approach for statistical process monitoring in the case of autocorrelated data is the residual chart. This chart requires that a suitable model has been identified for the time series of process observations before residuals can be obtained. In this work, a new neural-based procedure, which is alleviated from the need for building a time series model, is introduced for quality control in the case of serially correlated data. In particular, the Elman's recurrent neural network is proposed for manufacturing process quality control. Performance comparisons between the neural-based algorithm and several control charts are also presented in the paper in order to validate the approach. Different magnitudes of the process mean shift, under the presence of various levels of autocorrelation, are considered. The simulation results indicate that the neural-based procedure may perform better than other control charting schemes in several instances for both small and large shifts. Given the simplicity of the proposed neural network and its adaptability, this approach is proved from simulation experiments to be a feasible alternative for quality monitoring in the case of autocorrelated process data.