Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data

Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data

The presence of outliers is the main reason leading to ineffectiveness of advanced data-driven control methods in electric arc furnace systems. This paper proposes a hybrid method dedicated to detecting outliers in electric arc furnace systems, where process data are characterized as unlabeled, imba...

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Main Authors: Biao Wang, Zhizhong Mao
Format: Article
Language:English
Published: SAGE Publishing 2018-04-01
Series:Measurement + Control
Online Access:https://doi.org/10.1177/0020294018771097
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spelling doaj-bbcc49bc383c45fb81acddf85dafacb02020-11-25T03:34:21ZengSAGE PublishingMeasurement + Control0020-29402018-04-015110.1177/0020294018771097Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy DataBiao WangZhizhong MaoThe presence of outliers is the main reason leading to ineffectiveness of advanced data-driven control methods in electric arc furnace systems. This paper proposes a hybrid method dedicated to detecting outliers in electric arc furnace systems, where process data are characterized as unlabeled, imbalanced, non-stationary and noisy. First, the raw data are divided into certain number of clusters. Then, with each cluster, a one-class classifier can be trained. So with these well-trained sub-models, new test points can be investigated. Those points that are rejected by all sub-models will be labeled as outliers. With the combination of one-class classification and clustering technique, the intricate data in electric arc furnace can be processed effectively. In addition, the detector will be updated with a specific strategy to enhance its adaptiveness. A series of experiments are carried out, and comparative results have shown the effectiveness of our method.https://doi.org/10.1177/0020294018771097
collection DOAJ
language English
format Article
sources DOAJ
author Biao Wang
Zhizhong Mao
spellingShingle Biao Wang
Zhizhong Mao
Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data
Measurement + Control
author_facet Biao Wang
Zhizhong Mao
author_sort Biao Wang
title Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data
title_short Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data
title_full Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data
title_fullStr Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data
title_full_unstemmed Detecting Outliers in Electric Arc Furnace under the Condition of Unlabeled, Imbalanced, Non-stationary and Noisy Data
title_sort detecting outliers in electric arc furnace under the condition of unlabeled, imbalanced, non-stationary and noisy data
publisher SAGE Publishing
series Measurement + Control
issn 0020-2940
publishDate 2018-04-01
description The presence of outliers is the main reason leading to ineffectiveness of advanced data-driven control methods in electric arc furnace systems. This paper proposes a hybrid method dedicated to detecting outliers in electric arc furnace systems, where process data are characterized as unlabeled, imbalanced, non-stationary and noisy. First, the raw data are divided into certain number of clusters. Then, with each cluster, a one-class classifier can be trained. So with these well-trained sub-models, new test points can be investigated. Those points that are rejected by all sub-models will be labeled as outliers. With the combination of one-class classification and clustering technique, the intricate data in electric arc furnace can be processed effectively. In addition, the detector will be updated with a specific strategy to enhance its adaptiveness. A series of experiments are carried out, and comparative results have shown the effectiveness of our method.
url https://doi.org/10.1177/0020294018771097
work_keys_str_mv AT biaowang detectingoutliersinelectricarcfurnaceundertheconditionofunlabeledimbalancednonstationaryandnoisydata
AT zhizhongmao detectingoutliersinelectricarcfurnaceundertheconditionofunlabeledimbalancednonstationaryandnoisydata
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