├── README.md
├── TEP -  models.ipynb
└── TEP-1.ipynb


/README.md:
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 1 | # TEP-Fault-Detection
 2 | #Description
 3 | 
 4 | This dataverse contains the data referenced in Rieth et al. (2017). Issues and Advances in Anomaly Detection Evaluation for Joint Human-Automated Systems. To be presented at Applied Human Factors and Ergonomics 2017.
 5 | 
 6 | Each .RData file is an external representation of an R dataframe that can be read into an R environment with the 'load' function. The variables loaded are named ‘fault_free_training’, ‘fault_free_testing’, ‘faulty_testing’, and ‘faulty_training’, corresponding to the RData files.
 7 | 
 8 | Each dataframe contains 55 columns:
 9 | 
10 | Column 1 ('faultNumber') ranges from 1 to 20 in the “Faulty” datasets and represents the fault type in the TEP. The “FaultFree” datasets only contain fault 0 (i.e. normal operating conditions).
11 | 
12 | Column 2 ('simulationRun') ranges from 1 to 500 and represents a different random number generator state from which a full TEP dataset was generated (Note: the actual seeds used to generate training and testing datasets were non-overlapping).
13 | 
14 | Column 3 ('sample') ranges either from 1 to 500 (“Training” datasets) or 1 to 960 (“Testing” datasets). The TEP variables (columns 4 to 55) were sampled every 3 minutes for a total duration of 25 hours and 48 hours respectively. Note that the faults were introduced 1 and 8 hours into the Faulty Training and Faulty Testing datasets, respectively.
15 | 
16 | Columns 4 to 55 contain the process variables; the column names retain the original variable names.
17 | 


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