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...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2015-08, Vol.17 (8), p.5868-5887
Main Authors: Yu, Weijun, Yang, Fan
Format: Article
Language:English
Subjects:
alarm design
Alarm systems
binary alarm series
causality analysis
Computation
Computer programs
Computer simulation
Entropy
Mathematical models
Nonlinearity
Software
transfer entropy
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e17085868