Fault detection and diagnosis with random forest feature extraction and variable importance methods
The ever-present drive to safer, more cost-effective and cleaner processes motivates the exploration of a variety of process monitoring methods. In the domain of data-driven approaches, random forest models present a nonlinear framework. Random forest models consist of ensembles of classification an...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Elsevier
2010
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/27554 |
| _version_ | 1848752295784742912 |
|---|---|
| author | Aldrich, Chris Auret, L. |
| author2 | C Aldrich |
| author_facet | C Aldrich Aldrich, Chris Auret, L. |
| author_sort | Aldrich, Chris |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The ever-present drive to safer, more cost-effective and cleaner processes motivates the exploration of a variety of process monitoring methods. In the domain of data-driven approaches, random forest models present a nonlinear framework. Random forest models consist of ensembles of classification and regression trees in which the model response is determined by voting committees of independent binary decision trees. Data-driven approaches to fault diagnosis often involve summarizing potentially large numbers of process variables in lower dimensional diagnostic sequences. Random forest feature extraction allows for the monitoring of process in feature and residual spaces, while random forest variable importance measures can potentially be used to identify process variables contribution to fault conditions. In this study, a framework for diagnosing steady state faults with random forests is proposed and demonstrated with a simple nonlinear system and the benchmark Tennessee Eastman process. |
| first_indexed | 2025-11-14T08:06:21Z |
| format | Conference Paper |
| id | curtin-20.500.11937-27554 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:06:21Z |
| publishDate | 2010 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-275542023-01-13T07:56:31Z Fault detection and diagnosis with random forest feature extraction and variable importance methods Aldrich, Chris Auret, L. C Aldrich L Auret fault diagnosis feature extraction multivariate - statistical process control Random forest models variable importance The ever-present drive to safer, more cost-effective and cleaner processes motivates the exploration of a variety of process monitoring methods. In the domain of data-driven approaches, random forest models present a nonlinear framework. Random forest models consist of ensembles of classification and regression trees in which the model response is determined by voting committees of independent binary decision trees. Data-driven approaches to fault diagnosis often involve summarizing potentially large numbers of process variables in lower dimensional diagnostic sequences. Random forest feature extraction allows for the monitoring of process in feature and residual spaces, while random forest variable importance measures can potentially be used to identify process variables contribution to fault conditions. In this study, a framework for diagnosing steady state faults with random forests is proposed and demonstrated with a simple nonlinear system and the benchmark Tennessee Eastman process. 2010 Conference Paper http://hdl.handle.net/20.500.11937/27554 Elsevier restricted |
| spellingShingle | fault diagnosis feature extraction multivariate - statistical process control Random forest models variable importance Aldrich, Chris Auret, L. Fault detection and diagnosis with random forest feature extraction and variable importance methods |
| title | Fault detection and diagnosis with random forest feature extraction and variable importance methods |
| title_full | Fault detection and diagnosis with random forest feature extraction and variable importance methods |
| title_fullStr | Fault detection and diagnosis with random forest feature extraction and variable importance methods |
| title_full_unstemmed | Fault detection and diagnosis with random forest feature extraction and variable importance methods |
| title_short | Fault detection and diagnosis with random forest feature extraction and variable importance methods |
| title_sort | fault detection and diagnosis with random forest feature extraction and variable importance methods |
| topic | fault diagnosis feature extraction multivariate - statistical process control Random forest models variable importance |
| url | http://hdl.handle.net/20.500.11937/27554 |