Pages that link to "Item:Q2466911"
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The following pages link to Improved principal component monitoring using the local approach (Q2466911):
Displaying 15 items.
- Detecting abnormal situations using the Kullback-Leibler divergence (Q473296) (← links)
- Assessment of \(T^2\)- and \(Q\)-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring (Q509339) (← links)
- Batch process monitoring based on multilevel ICA-PCA (Q946644) (← links)
- Fault detection for industrial processes (Q1955156) (← links)
- Hybrid variable monitoring: an unsupervised process monitoring framework with binary and continuous variables (Q2103637) (← links)
- A novel multivariate statistical process monitoring algorithm: orthonormal subspace analysis (Q2123220) (← links)
- A generalized probabilistic monitoring model with both random and sequential data (Q2165960) (← links)
- Canonical correlation analysis-based explicit relation discovery for statistical process monitoring (Q2181391) (← links)
- Multivariate statistical process monitoring based on principal discriminative component analysis (Q2235402) (← links)
- Recursive transformed component statistical analysis for incipient fault detection (Q2407206) (← links)
- Local structural analysis of data realizations (Q2487515) (← links)
- Detection of intermittent faults based on an optimally weighted moving average \(T^2\) control chart with stationary observations (Q2662261) (← links)
- Multivariate statistical process monitoring and diagnosis with grouped regression‐adjusted variables (Q4266847) (← links)
- Multivariate Process Monitoring Using the Dynamic Biplot (Q4379689) (← links)
- Improved process monitoring using nonlinear principal component models (Q5387047) (← links)
