Fault detection and diagnosis with random forest feature extraction and variable importance methods

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...

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Bibliographic Details
Main Authors: Aldrich, Chris, Auret, L.
Other Authors: C Aldrich
Format: Conference Paper
Published: Elsevier 2010
Subjects:
fault diagnosis
feature extraction
multivariate - statistical process control
Random forest models
variable importance
Online Access:http://hdl.handle.net/20.500.11937/27554

Internet

http://hdl.handle.net/20.500.11937/27554

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