Fault diagnosis performance improvement for chemical process based on EasyEnsemble method
XIA Li-sha
YANG Yu-ying
FANG Hua-jing
Abstract：Imbalanced dataset is a phenomenon existing massively in the field of chemical process fault diagnosis. The recognition rate of the classifier will be biased to the majority class samples when using imbalanced dataset as the training set. As a result, the normal state is easy to identify, while the fault state people concerned are difficult to be diagnosed. In this paper, an EasyEnsemble based principle component analysis-support vector machine (EEPS) fault diagnosis algorithm is proposed. After constructing a number of balanced subsets by under-sampling from the majority class, principle component analysis (PCA) is used for feature extraction and a number of support vector machine (SVM) sub-classifiers are trained accordingly. Then an integral classifier is developed by using the Adaboost algorithm. This integral classifier can be used for fault diagnosis and prognosis. The experimental results on Tenessee Eastman (TE) chemical process show that the proposed EEPS improves the diagnosis and prognosis performance on the imbalanced dataset.
Keywords：chemical processimbalanced datasetEasyEnsemblefault diagnosis
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 49-53 )