Abstract
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.
Highlights
Alarms are indications of abnormal situations in process industries, including food, beverages, chemicals, pharmaceuticals, petroleum, ceramics, base metals, coal, plastics, rubber, textiles, tobacco, wood and wood products, paper and paper products, etc, where the primary production processes are either continuous or occur on a batch of materials that is indistinguishable [1]
The transfer entropy (TE) technique using continuous data has been widely used, the application to discrete time series is in its infancy
For the reasons mentioned in the Introduction, the application of discrete time series should receive more attention
Summary
Alarms are indications of abnormal situations in process industries, including food, beverages, chemicals, pharmaceuticals, petroleum, ceramics, base metals, coal, plastics, rubber, textiles, tobacco, wood and wood products, paper and paper products, etc, where the primary production processes are either continuous or occur on a batch of materials that is indistinguishable [1]. Such a situation often leads to alarm floods In this case, it is difficult for operators to identify the type of fault or to find its root cause to mitigate the source of the abnormality. It is difficult for operators to identify the type of fault or to find its root cause to mitigate the source of the abnormality Without proper actions, such a situation may lead to serious and catastrophic events. For bivariate or multivariate situations, Folmer et al [4] summarized several approaches Among these approaches, it is an essential method to identify the propagation paths between variables and, to localize the root cause of the abnormal situation. The main contribution of this paper is a new application of TE to identify causality between variables by using binary alarm data in general multivariate systems. This paper discrete avoid estimating high dimensional pdfs using a technique of symbolization discrete use natural binary alarm data for causality detection
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