Detecting faults in process systems with singular spectrum analysis
In this study, process monitoring based on signal decomposition by use of singular spectrum analysis (SSA) is considered. SSA makes use of adaptive basis functions to decompose a time series into multiple components that may be periodic, aperiodic or random. Two variants of SSA are considered in thi...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/7250 |
| _version_ | 1848745315642900480 |
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| author | Krishnannair, S. Aldrich, Chris Jemwa, G. |
| author_facet | Krishnannair, S. Aldrich, Chris Jemwa, G. |
| author_sort | Krishnannair, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this study, process monitoring based on signal decomposition by use of singular spectrum analysis (SSA) is considered. SSA makes use of adaptive basis functions to decompose a time series into multiple components that may be periodic, aperiodic or random. Two variants of SSA are considered in this investigation. In the first, the conventional approach is used based on latent variables extracted from the covariances of the lagged trajectory matrix of the process variables. The second approach is identical to the first approach, except that the covariances of the lagged trajectory matrices are replaced by Euclidean distance dissimilarities to decompose the variables into additive components. These components are subsequently monitored and the merits of the two approaches are considered on the basis of two case studies using simulated nonlinear data and data from the benchmark Tennessee Eastman process. |
| first_indexed | 2025-11-14T06:15:25Z |
| format | Journal Article |
| id | curtin-20.500.11937-7250 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:15:25Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-72502017-09-13T14:39:53Z Detecting faults in process systems with singular spectrum analysis Krishnannair, S. Aldrich, Chris Jemwa, G. In this study, process monitoring based on signal decomposition by use of singular spectrum analysis (SSA) is considered. SSA makes use of adaptive basis functions to decompose a time series into multiple components that may be periodic, aperiodic or random. Two variants of SSA are considered in this investigation. In the first, the conventional approach is used based on latent variables extracted from the covariances of the lagged trajectory matrix of the process variables. The second approach is identical to the first approach, except that the covariances of the lagged trajectory matrices are replaced by Euclidean distance dissimilarities to decompose the variables into additive components. These components are subsequently monitored and the merits of the two approaches are considered on the basis of two case studies using simulated nonlinear data and data from the benchmark Tennessee Eastman process. 2016 Journal Article http://hdl.handle.net/20.500.11937/7250 10.1016/j.cherd.2016.07.014 Elsevier restricted |
| spellingShingle | Krishnannair, S. Aldrich, Chris Jemwa, G. Detecting faults in process systems with singular spectrum analysis |
| title | Detecting faults in process systems with singular spectrum analysis |
| title_full | Detecting faults in process systems with singular spectrum analysis |
| title_fullStr | Detecting faults in process systems with singular spectrum analysis |
| title_full_unstemmed | Detecting faults in process systems with singular spectrum analysis |
| title_short | Detecting faults in process systems with singular spectrum analysis |
| title_sort | detecting faults in process systems with singular spectrum analysis |
| url | http://hdl.handle.net/20.500.11937/7250 |