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.\r\n\r\n
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.\r\n\r\n
Each dataframe contains 55 columns:\r\n\r\n
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).\r\n\r\n
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).\r\n\r\n
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.\r\n\r\n
Columns 4 to 55 contain the process variables; the column names retain the original variable names.\r\n\r\n
Acknowledgments. This work was sponsored by the Office of Naval Research, Human & Bioengineered Systems (ONR 341), program officer Dr. Jeffrey G. Morrison under contract N00014-15-C-5003. The views expressed are those of the authors and do not reflect the official policy or position of the Office of Naval Research, Department of Defense, or US Government.","citation:dsDescriptionDate":"2017-06-23"},"author":[{"citation:authorName":"Rieth, Cory A.","citation:authorAffiliation":"Pacific Science and Engineering Group, Inc"},{"citation:authorName":"Amsel, Ben D. ","citation:authorAffiliation":"Pacific Science and Engineering Group, Inc"},{"citation:authorName":"Tran, Randy ","citation:authorAffiliation":"Pacific Science and Engineering Group, Inc"},{"citation:authorName":"Cook, Maia B.","citation:authorAffiliation":"Pacific Science and Engineering Group, Inc"}],"subject":["Engineering","Computer and Information Science","Social Sciences"],"title":"Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation","citation:depositor":"Rieth, Cory","dateOfDeposit":"2017-03-10","@id":"https://doi.org/10.7910/DVN/6C3JR1","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.0","schema:name":"Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation","schema:dateModified":"Thu Jul 06 11:23:33 UTC 2017","schema:datePublished":"2017-07-06","schema:creativeWorkStatus":"RELEASED","dvcore:termsOfUse":"