Please use this identifier to cite or link to this item:
|Title:||Multivariate temporal data analysis using self-organizing Maps. 2. Monitoring and diagnosis of multistate operations|
|Authors:||Ng, Y.S. |
|Source:||Ng, Y.S., Srinivasan, R. (2008-10-15). Multivariate temporal data analysis using self-organizing Maps. 2. Monitoring and diagnosis of multistate operations. Industrial and Engineering Chemistry Research 47 (20) : 7758-7771. ScholarBank@NUS Repository. https://doi.org/10.1021/ie071022y|
|Abstract:||The operation of transitions in continuous processes is challenging and often results in out-of-spec products, alarm floods, and abnormal situations. Therefore, efficient techniques for automated monitoring and fault diagnosis of such operations are essential. In Part 1 of this paper, we proposed a self-organizing map (SOM) training strategy for effectively visualizing multistate operations. In this part of the series, we use the same methodology as a representation scheme to compare operating trajectories and diagnosing faults during transient operations. In the proposed approach, clusters of SOM neurons, called neuronal clusters, serve as landmarks on the multivariate measurement space. Online data during the transition are reflected as a trajectory on the SOM and are converted to a sequence of neuronal clusters, which are the signature of the operating state. We have adapted the well-known Smith and Waterman discrete sequence comparison algorithm from bioinformatics to compare the state signatures and account for run-to-run variations. The proposed comparison method accounts explicitly for oscillations that are common in chemical processes. Online monitoring and diagnosis is performed by comparing the signature with those of known normal and abnormal transitions. The key advantage of the proposed strategy are its computational speed, inherent multivariate nature, and robustness to run-to-run variations, in addition to intuitiveness and visualization of the results. We illustrate the proposed method through two case studies: the Tennessee Eastman challenge problem and startup of a laboratory-scale distillation unit. © 2008 American Chemical Society.|
|Source Title:||Industrial and Engineering Chemistry Research|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 28, 2018
WEB OF SCIENCETM
checked on Mar 5, 2018
checked on Apr 20, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.