Statistical process monitoring of a multiphase flow facility

Simple item page
dc.contributor.authorRuiz Carcel, Cristobal
dc.contributor.authorCao, Yi
dc.contributor.authorHarrison, David
dc.contributor.authorLao, Liyun
dc.contributor.authorSamuel, Raphael
dc.date.accessioned2016-10-04T08:47:13Z
dc.date.available2016-10-04T08:47:13Z
dc.date.issued2015-06-16
dc.description.abstractIndustrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms.en_UK
dc.identifier.citationRuiz Carcel C et al. Statistical process monitoring of a multiphase flow facility, Statistical process monitoring of a multiphase flow facility, Volume 42, September 2015, Pages 74–88.en_UK
dc.identifier.cris4412836
dc.identifier.isbnhttp://dx.doi.org/10.1016/j.conengprac.2015.04.012
dc.identifier.issn0967-0661
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10650
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-Non-Commercial-No Derivatives 3.0 Unported (CC BY-NC-ND 3.0). You are free to: Share — copy and redistribute the material in any medium or format. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No Derivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectfault detectionen_UK
dc.subjectdiagnosisen_UK
dc.subjectmultivariateen_UK
dc.subjectcanonicalen_UK
dc.subjectexperimentalen_UK
dc.subjectprocessen_UK
dc.titleStatistical process monitoring of a multiphase flow facilityen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Statistical_Process_Monitoring_of_a_Multiphase_Flow_Facility-2015.pdf
Size:
501.01 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.79 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)