Version 1.0.0

Fault detection:

Simulation datasets:

• Generated a normal_training.csv dataset
• Generated a normal_testing.csv dataset
• Generated a faults_training.csv dataset

PCA:

• Performed PCA on the normal_training.csv dataset
• Created a scree plot for the normal_training.csv dataset
• Reduced the order of the normal_training.csv dataset

Fault detection training:

• Determined the Hotelling's T² statistic of the normal_training.csv dataset
• Determined the Q statistic of the normal_training.csv dataset
• Constructed the Hotelling's T² statistic and Q statistic control charts for the normal_training.csv dataset
• Set the Hotelling's T²_α and Q_α upper control limits for the normal_training.csv dataset

Fault detection:

• Used the training PCA loadings from the normal_training.csv dataset to reduce the faults_training.csv dataset
• Determined the Hotelling's T² statistic of the faults_training.csv dataset
• Determined the Q statistic of the faults_training.csv dataset
• Constructed the Hotelling's T² statistic and Q statistic control charts for the faults_training.csv dataset
• Detected the process faults in the faults_training.csv dataset
• Calculated the ROC curve parameters for the Hotelling's T² statistic and Q statistic

ROC curves:

• Generated a Hotelling's T² ROC curve for the faults_training.csv dataset
• Generated a Q statistic ROC curve for the faults_training.csv dataset