A heterogeneous benchmark dataset for data analytics: multiphase flow facility case study
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2019-07-01
Date Acceptance
2019-04-24
Citation
Journal of Process Control, 2019, 79 (1), pp.41-55
ISSN
0959-1524
Publisher
Elsevier
Start Page
41
End Page
55
Journal / Book Title
Journal of Process Control
Volume
79
Issue
1
Copyright Statement
©2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC-BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Sponsor
Commission of the European Communities
Commission of the European Communities
Identifier
https://www.sciencedirect.com/science/article/pii/S0959152418303603?via%3Dihub
Grant Number
675215
675215
Subjects
Science & Technology
Technology
Automation & Control Systems
Engineering, Chemical
Engineering
Process monitoring
Condition monitoring
Data analytics
Induced faults
PRONTO benchmark dataset
Fault detection and diagnosis
FAULT-DETECTION
DIAGNOSTICS
PROGNOSTICS
SELECTION
SYSTEMS
0904 Chemical Engineering
Chemical Engineering
Publication Status
Published
Date Publish Online
2019-05-16