PRONTO heterogeneous benchmark dataset

<p>The PRONTO heterogeneous benchmark dataset is based on an industrial-scale multiphase flow facility. It includes data from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements, an operation log and video recordings. The study collected data from various operational conditions with and without induced faults to generate a multi-rate, multi-modal dataset. The dataset is suitable for developing and validating algorithms for fault detection and diagnosis (FDD) and data fusion.&nbsp;</p> <p>When using the dataset please cite the following publication:</p> <p>A. Stief, R. Tan, Y. Cao, J. R. Ottewill, N. F. Thornhill, J. Baranowski,&nbsp;A heterogeneous benchmark dataset for data analytics: Multiphase flow facility case study, Journal of Process Control, 79 (2019) 41&ndash;55, DOI:&nbsp;<a href="https://doi.org/10.1016/j.jprocont.2019.04.009">https://doi.org/10.1016/j.jprocont.2019.04.009</a></p> <p>The dataset has been used in the following works:</p> <p>A.&nbsp;Stief, R.&nbsp;Tan, Y.&nbsp;Cao, J. R. Ottewill. Analytics of heterogeneous process data: Multiphase flow facility case study. IFAC-PapersOnLine, 51(18):363&ndash;368, 2018. DOI: <a href="https://doi.org/10.1016/j.ifacol.2018.09.327">https://doi.org/10.1016/j.ifacol.2018.09.327</a></p> <p>A.&nbsp;Stief, J. R. Ottewill, R. Tan, Y.&nbsp;Cao. Process and alarm data integration under a two-stage Bayesian framework for fault diagnostics. IFAC-PapersOnLine, 51(24):1220&ndash;1226, 2018. DOI: <a href="https://doi.org/10.1016/j.ifacol.2018.09.696">https://doi.org/10.1016/j.ifacol.2018.09.696</a></p> <p>A. Stief, J. R. Ottewill,&nbsp;J. Baranowski. Investigation of the diagnostic properties of sensors and features in a multiphase flow facility case study. in: 12^th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019</p> <p>M.&nbsp;Lucke, X. Mei, A. Stief, M. Chioua, N. F. Thornhill. Variable selection for fault detection and identification based on mutual information of multi-valued alarm series,&nbsp;in: 12^th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019</p> <p>R. Tan, T. Cong, N. F. Thornhill, J. R. Ottewill, J. Baranowski. Statistical monitoring of processes with multiple operating modes, in: 12^th IFAC Symposium on Dynamics and Control of Process Systems (in press), 2019.</p>