Feature selection for fault detection systems: application to the Tennessee Eastman process. Chebel-Morello, B., Malinowski, S., & Senoussi, H. Applied Intelligence, 44(1):111–122, January, 2016. ![Feature selection for fault detection systems: application to the Tennessee Eastman process [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In fault detection systems, a massive amount of data gathered from the life-cycle of equipment is often used to learn models or classifiers that aims at diagnosing different kinds of errors or failures. Among this huge quantity of information, some features (or sets of features) are more correlated with a kind of failure than another. The presence of irrelevant features might affect the performance of the classifier. To improve the performance of a detection system, feature selection is hence a key step. We propose in this paper an algorithm named STRASS, which aims at detecting relevant features for classification purposes. In certain cases, when there exists a strong correlation between some features and the associated class, conventional feature selection algorithms fail at selecting the most relevant features. In order to cope with this problem, STRASS algorithm uses k-way correlation between features and the class to select relevant features. To assess the performance of STRASS, we apply it on simulated data collected from the Tennessee Eastman chemical plant simulator. The Tennessee Eastman process (TEP) has been used in many fault detection studies and three specific faults are not well discriminated with conventional algorithms. The results obtained by STRASS are compared to those obtained with reference feature selection algorithms. We show that the features selected by STRASS always improve the performance of a classifier compared to the whole set of original features and that the obtained classification is better than with most of the other feature selection algorithms.
@article{chebel-morello_feature_2016,
title = {Feature selection for fault detection systems: application to the {Tennessee} {Eastman} process},
volume = {44},
issn = {1573-7497},
shorttitle = {Feature selection for fault detection systems},
url = {https://doi.org/10.1007/s10489-015-0694-6},
doi = {10.1007/s10489-015-0694-6},
abstract = {In fault detection systems, a massive amount of data gathered from the life-cycle of equipment is often used to learn models or classifiers that aims at diagnosing different kinds of errors or failures. Among this huge quantity of information, some features (or sets of features) are more correlated with a kind of failure than another. The presence of irrelevant features might affect the performance of the classifier. To improve the performance of a detection system, feature selection is hence a key step. We propose in this paper an algorithm named STRASS, which aims at detecting relevant features for classification purposes. In certain cases, when there exists a strong correlation between some features and the associated class, conventional feature selection algorithms fail at selecting the most relevant features. In order to cope with this problem, STRASS algorithm uses k-way correlation between features and the class to select relevant features. To assess the performance of STRASS, we apply it on simulated data collected from the Tennessee Eastman chemical plant simulator. The Tennessee Eastman process (TEP) has been used in many fault detection studies and three specific faults are not well discriminated with conventional algorithms. The results obtained by STRASS are compared to those obtained with reference feature selection algorithms. We show that the features selected by STRASS always improve the performance of a classifier compared to the whole set of original features and that the obtained classification is better than with most of the other feature selection algorithms.},
language = {en},
number = {1},
urldate = {2022-05-02},
journal = {Applied Intelligence},
author = {Chebel-Morello, Brigitte and Malinowski, Simon and Senoussi, Hafida},
month = jan,
year = {2016},
keywords = {Contextual measure, Fault detection, Feature selection, Wrapper method},
pages = {111--122},
}
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