Multi-State Adaptive Dynamic Principal Component Analysis for Multivariate Process Monitoring

dataStateSwitch | Alternate Observations in a Data Frame over States |

fault1A_xts | Process Data under a System Shift Fault |

fault2A_xts | Process Data under a System Drift Fault |

fault3A_xts | Process Data under a System Signal Amplification |

faultDetect | Process Fault Detection |

faultFilter | Process Fault Filtering |

faultSwitch | Induce the Specified Fault on NOC Observations |

mspContributionPlot | Contribution Plots |

mspMonitor | Real-Time Process Monitoring Function |

mspProcessData | Simulate Normal or Fault Observations from a Single-State or... |

mspSPEPlot | Squared Prediction Error Contribution Plots |

mspSubset | Multi-State Subsetting |

mspT2Plot | T-Squared Contribution Plots |

mspTrain | Multi-State Adaptive-Dynamic Process Training |

mspWarning | Process Alarms |

mvMonitoring | A Package for Multivariate Statistical Process Monitoring |

normal_switch_xts | Process Data under Normal Conditions |

oneDay_clean | Real Process Data for Testing |

pca | PCA for Data Scatter Matrix |

processMonitor | Adaptive Process Training |

processNOCdata | Simulate NOC Observations from a Single-State or Multi-State... |

rotate3D | Three-Dimensional Rotation Matrix |

rotateScale3D | Three-Dimensional Rotation and Scaling Matrix |

tenDay_clean | Real Process Data for Training |

threshold | Non-parametric Threshold Estimation |

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