General Diagnostic Framework Based on Non-axiomatic Logic for Aviation Safety Event Analysis

JIANG Yang; WANG Hong; FENG Xingjie

doi:10.1049/cje.2019.07.010

Volume 28 Issue 6

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Article Navigation > Chinese Journal of Electronics > 2019 > 28(6): 1152-1157

JIANG Yang, WANG Hong, FENG Xingjie, “General Diagnostic Framework Based on Non-axiomatic Logic for Aviation Safety Event Analysis,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1152-1157, 2019, doi: 10.1049/cje.2019.07.010

Citation:

JIANG Yang, WANG Hong, FENG Xingjie, “General Diagnostic Framework Based on Non-axiomatic Logic for Aviation Safety Event Analysis,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1152-1157, 2019, doi: 10.1049/cje.2019.07.010

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General Diagnostic Framework Based on Non-axiomatic Logic for Aviation Safety Event Analysis

doi: 10.1049/cje.2019.07.010

School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

Funds: This work is supported by the Fundamental Research Funds for the Central Universities(No.3122015C022) and the National Natural Science Foundation of China(No.U1633110).

Received Date: 2019-03-04
Rev Recd Date: 2019-07-24
Publish Date: 2019-11-10

Abstract

Abstract

To achieve causality reasoning of aviation safety events based on big data of cross-media network, a data-driven general diagnostic framework based on nonaxiomatic logic is designed and implemented. On the basis of this framework, the uncertain causality between aviation safety events and faults is expressed in the form of binary non-axiomatic incident experience at first. A general expression for calculating the attribution and confidence degrees in the non-axiomatic incident experience is given based on records of aviation safety historical incident. A concept of non-axiomatic incident experience graph is proposed, a diagnosis algorithm for aviation safety events is given with the combination of revision and deduction rules in non-axiomatic logic. Experimental results of a Version 1.0 beta demo show that this framework can effectively diagnose all potential faults according to aviation safety events; compared with other machine learning frameworks, it has higher reliability (especially scalability) under the premise of ensuring diagnosis accuracy.
- Cross-media,
- Uncertain reasoning,
- Diagnostic framework,
- Interpretable,
- Scalability

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References(23)

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