Abstract

Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents. However, it is a complicated work to find causality among events in a CEEG. This paper presents a method to accurately extract event causality by using a neural network and structural analysis. First, we identify the events and their component elements from fault trees by natural language processing technology. Then, causality in accident events is divided into explicit causality and implicit causality. Explicit causality is obtained by analyzing the hierarchical structure relations of event nodes and the semantics of component logic gates in fault trees. By integrating internal structural features of events and semantic features of event sentences, we extract implicit causality by utilizing a bidirectional gated recurrent unit (BiGRU) neural network. An algorithm, named CEFTAR, is presented to extract causality for safety events in chemical accidents from fault trees and accident reports. Compared with the existing methods, experimental results show that our method has a higher accuracy and recall rate in extracting causality.

Highlights

  • In recent years, the chemical industry has made tremendous contributions to economic and social development

  • We concern about how to identify events and extract causal relationships between these events. e main contributions of this study are as follows: (1) We propose an effective method to extract event elements by combining fault tree with accident reports. e combination of fault tree and accident report greatly reduces the complexity of event extraction based on NLP

  • Implicit causality is generated based on bidirectional gated recurrent unit (BiGRU) neural network by feeding internal structural features of events and semantic features of event sentences. e accuracy and efficiency of extracting causality are improved by dividing causality into explicit causality and implicit causality

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Summary

Introduction

The chemical industry has made tremendous contributions to economic and social development. To compensate for the abovementioned deficiencies of the fault trees in safety analysis, early warning, and emergency disposal, event evolutionary graph (EEG) is introduced to model the event evolution process in chemical accidents in this paper. (1) We propose an effective method to extract event elements by combining fault tree with accident reports. (2) We obtain explicit causality by analyzing hierarchical structure relations of event nodes and logic gates in fault trees. To accurately and automatically acquire the knowledge in building CEEG, we proposed a method to extract the events and causality from fault trees and accident reports. We will present the definitions of fault tree and EEG so as to better illustrate our method . To find causality between two events is a more difficult and challenging work

Event Identification
Extraction of Event Causality
Experiment and Analysis
Conclusions
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