Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy
In modern industrial processes, it is easier and less expensive to configure alarms by software settings rather than by wiring, which causes the rapid growth of the number of alarms. Moreover, because there exist complex interactions, in particular the causal relationship among different parts in th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-08-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/17/8/5868 |
| _version_ | 1818473946079559680 |
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| author | Weijun Yu Fan Yang |
| author_facet | Weijun Yu Fan Yang |
| author_sort | Weijun Yu |
| collection | DOAJ |
| description | In modern industrial processes, it is easier and less expensive to configure alarms by software settings rather than by wiring, which causes the rapid growth of the number of alarms. Moreover, because there exist complex interactions, in particular the causal relationship among different parts in the process, a fault may propagate along propagation pathways once an abnormal situation occurs, which brings great difficulty to operators to identify its root cause immediately and to take proper actions correctly. Therefore, causality detection becomes a very important problem in the context of multivariate alarm analysis and design. Transfer entropy has become an effective and widely-used method to detect causality between different continuous process variables in both linear and nonlinear situations in recent years. However, such conventional methods to detect causality based on transfer entropy are computationally costly. Alternatively, using binary alarm series can be more computational-friendly and more direct because alarm data analysis is straightforward for alarm management in practice. The methodology and implementation issues are discussed in this paper. Illustrated by several case studies, including both numerical cases and simulated industrial cases, the proposed method is demonstrated to be suitable for industrial situations contaminated by noise. |
| first_indexed | 2024-04-14T04:29:44Z |
| format | Article |
| id | doaj.art-925046d4940c4428971ae30d685c2344 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T04:29:44Z |
| publishDate | 2015-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-925046d4940c4428971ae30d685c23442022-12-22T02:12:06ZengMDPI AGEntropy1099-43002015-08-011785868588710.3390/e17085868e17085868Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer EntropyWeijun Yu0Fan Yang1Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, ChinaTsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, ChinaIn modern industrial processes, it is easier and less expensive to configure alarms by software settings rather than by wiring, which causes the rapid growth of the number of alarms. Moreover, because there exist complex interactions, in particular the causal relationship among different parts in the process, a fault may propagate along propagation pathways once an abnormal situation occurs, which brings great difficulty to operators to identify its root cause immediately and to take proper actions correctly. Therefore, causality detection becomes a very important problem in the context of multivariate alarm analysis and design. Transfer entropy has become an effective and widely-used method to detect causality between different continuous process variables in both linear and nonlinear situations in recent years. However, such conventional methods to detect causality based on transfer entropy are computationally costly. Alternatively, using binary alarm series can be more computational-friendly and more direct because alarm data analysis is straightforward for alarm management in practice. The methodology and implementation issues are discussed in this paper. Illustrated by several case studies, including both numerical cases and simulated industrial cases, the proposed method is demonstrated to be suitable for industrial situations contaminated by noise.http://www.mdpi.com/1099-4300/17/8/5868alarm designtransfer entropybinary alarm seriescausality analysis |
| spellingShingle | Weijun Yu Fan Yang Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy Entropy alarm design transfer entropy binary alarm series causality analysis |
| title | Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy |
| title_full | Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy |
| title_fullStr | Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy |
| title_full_unstemmed | Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy |
| title_short | Detection of Causality between Process Variables Based on Industrial Alarm Data Using Transfer Entropy |
| title_sort | detection of causality between process variables based on industrial alarm data using transfer entropy |
| topic | alarm design transfer entropy binary alarm series causality analysis |
| url | http://www.mdpi.com/1099-4300/17/8/5868 |
| work_keys_str_mv | AT weijunyu detectionofcausalitybetweenprocessvariablesbasedonindustrialalarmdatausingtransferentropy AT fanyang detectionofcausalitybetweenprocessvariablesbasedonindustrialalarmdatausingtransferentropy |