In this section
@article{Stief:2019:10.1016/j.jprocont.2019.04.009,
author = {Stief, A and Tan, R and Cao, Y and Ottewill, JR and Thornhill, N and Baranowski, J},
doi = {10.1016/j.jprocont.2019.04.009},
journal = {Journal of Process Control},
pages = {41--55},
title = {A heterogeneous benchmark dataset for data analytics: multiphase flow facility case study},
url = {http://dx.doi.org/10.1016/j.jprocont.2019.04.009},
volume = {79},
year = {2019}
}
TY - JOUR
AB - Improvements in sensing, connectivity and computing technologies mean that industrial processes now generate data from a variety of disparate sources. Data may take a number of forms, from time-domain signals, sampled at various rates using a variety of sensors, to alarm and event logs. Novel techniques need to be developed to tackle the challenges of heterogeneous data. Testing such algorithms requires benchmark datasets that allow direct comparison of the performance of the methods. This work presents the PRONTO heterogeneous benchmark dataset. Experiments were conducted on a multiphase flow facility under various operational conditions with and without induced faults. Data were collected from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements. The presented dataset is suitable for developing and validating algorithms for fault detection and diagnosis and data fusion concepts. Three algorithms are tested using the dataset, illustrating the applicability of the dataset.
AU - Stief,A
AU - Tan,R
AU - Cao,Y
AU - Ottewill,JR
AU - Thornhill,N
AU - Baranowski,J
DO - 10.1016/j.jprocont.2019.04.009
EP - 55
PY - 2019///
SN - 0959-1524
SP - 41
TI - A heterogeneous benchmark dataset for data analytics: multiphase flow facility case study
T2 - Journal of Process Control
UR - http://dx.doi.org/10.1016/j.jprocont.2019.04.009
UR - https://www.sciencedirect.com/science/article/pii/S0959152418303603?via%3Dihub
UR - http://hdl.handle.net/10044/1/70318
VL - 79
ER -
Nina Thornhill, ABB/RAEng Professor of Process Automation
Centre for Process Systems Engineering
Department of Chemical Engineering
Imperial College London
South Kensington Campus, London SW7 2AZ
Tel: +44 (0)20 7594 6622
Email: n.thornhill@imperial.ac.uk