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On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

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References

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2009A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks89 cited

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1989Synthetic neural networks for process control88 cited

2006On‐line Identification and Quantification of Mean Shifts in Bivariate Processes using a Neural Network‐based Approach85 cited

Cited by

2020Monitoring linear profiles using Artificial Neural Networks with run rules38 cited
2012On line detection of mean and variance shift using neural networks and support vector machine in multivariate processes33 cited
2013Monitoring and diagnosing of mean shifts in multivariate manufacturing processes using two-level selective ensemble of learning vector quantization neural networks26 cited
2016Applying emerging soft computing approaches to control chart pattern recognition for an SPC–EPC process24 cited
2023New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification18 cited
2013A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines17 cited
2022An SVM-GA based monitoring system for pattern recognition of autocorrelated processes16 cited
2014Interpreting out-of-control signals using instance-based bayesian classifier in multivariate statistical process control16 cited
2021Adaptive Shewhart Control Charts Under Fuzzy Parameters with Tuned Particle Swarm Optimization Algorithm13 cited
2026A review of artificial intelligence impacting statistical process monitoring and future directions12 cited
2023Root cause analysis of an out-of-control process using a logical analysis of data regression model and exponential weighted moving average11 cited
2014Bivariate quality control using two-stage intelligent monitoring scheme10 cited