Mathematical and Algorithmic Advances in Machine Learning for Statistical Process Control: A Systematic Review [PDF]
EntropyIntegrating machine learning (ML) with Statistical Process Control (SPC) is important for Industry 4.0 environments. Contemporary manufacturing data exhibit high-dimensionality, autocorrelation, non-stationarity, and class imbalance, which challenge ...
Yulong Qiao +5 more
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Use of Statistical Process Control for Coking Time Monitoring
Mathematics, 2023 Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts.
Marta Benková +3 more
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An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations
Energies, 2022 With the development of intelligent manufacturing, automated data acquisition techniques are widely used. The autocorrelations between data that are collected from production processes have become more common.
Yaping Li +3 more
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High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates
IEEE Access, 2022 The advent of modern data acquisition and computing techniques has enabled high-speed monitoring of high-dimensional processes. The short sampling interval makes the samples temporally correlated, even if there is no underlying autocorrelation among ...
Sangahn Kim +2 more
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A Comparative Study on a Triple-Concept Model of Two Techniques for Monitoring the Mean of Stationary Processes [PDF]
International Journal of Industrial Engineering and Production Research, 2021 In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems.
Samrad Jafarian-Namin +4 more
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IEEE Access, 2022 In statistical process monitoring, it is often assumed that the sequential observations generated by processes are independent and identically distributed (iid).
Yi Li, Tahir Munir, Xuelong Hu
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A Novel Methodology for Monitoring and Control of Non Stationary Processes Using Model-Based Control Charts (Case Study: bottomhole Pressure during Drilling Operations) [PDF]
International Journal of Industrial Engineering and Production Research, 2020 This work used two methods for Monitoring and control of autocorrelated processes based on time series modeling. The first method was the simultaneous monitoring of common and assignable causes. This method included applying five steps of data gathering,
Mahdi Imanian +2 more
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Algorithms, 2023 Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption.
Massimo Pacella +2 more
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A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes
Mathematics, 2023 Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes.
Cang Wu +4 more
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General simulation algorithm for autocorrelated binary processes [PDF]
Physical Review E, 2017 The apparent ubiquity of binary random processes in physics and many other fields has attracted considerable attention from the modeling community. However, generation of binary sequences with prescribed autocorrelation is a challenging task owing to the discrete nature of the marginal distributions, which makes the application of classical spectral ...
Serinaldi F, Lombardo F
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