Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks

The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodyna...

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Autores principales:	Keke Gao, Wenbin Feng, Xia Zhao, Chongchong Yu, Weijun Su, Yuqing Niu, Lu Han
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/5abe1d76b42447f582c2fece0e17a8aa
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spelling	oai:doaj.org-article:5abe1d76b42447f582c2fece0e17a8aa2021-11-29T00:57:06ZAnomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks1099-052610.1155/2021/5854096https://doaj.org/article/5abe1d76b42447f582c2fece0e17a8aa2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5854096https://doaj.org/toc/1099-0526The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.Keke GaoWenbin FengXia ZhaoChongchong YuWeijun SuYuqing NiuLu HanHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Keke Gao Wenbin Feng Xia Zhao Chongchong Yu Weijun Su Yuqing Niu Lu Han Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks
description	The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.
format	article
author	Keke Gao Wenbin Feng Xia Zhao Chongchong Yu Weijun Su Yuqing Niu Lu Han
author_facet	Keke Gao Wenbin Feng Xia Zhao Chongchong Yu Weijun Su Yuqing Niu Lu Han
author_sort	Keke Gao
title	Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks
title_short	Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks
title_full	Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks
title_fullStr	Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks
title_full_unstemmed	Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks
title_sort	anomaly detection for time series with difference rate sample entropy and generative adversarial networks
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/5abe1d76b42447f582c2fece0e17a8aa
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Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks

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