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Safety + ai: A novel approach to update safety models using artificial intelligence

Gheraibia, Youcef; Kabir, Sohag; Aslansefat, Koorosh; Sorokos, Ioannis; Papadopoulos, Yiannis

Authors

Youcef Gheraibia

Sohag Kabir

Ioannis Sorokos



Abstract

Safety-critical systems are becoming larger and more complex to obtain a higher level of functionality. Hence, modeling and evaluation of these systems can be a difficult and error-prone task. Among existing safety models, Fault Tree Analysis (FTA) is one of the well-known methods in terms of easily understandable graphical structure. This study proposes a novel approach by using Machine Learning (ML) and real-time operational data to learn about the normal behavior of the system. Afterwards, if any abnormal situation arises with reference to the normal behavior model, the approach tries to find the explanation of the abnormality on the fault tree and then share the knowledge with the operator. If the fault tree fails to explain the situation, a number of different recommendations, including the potential repair of the fault tree, are provided based on the nature of the situation. A decision tree is utilized for this purpose. The effectiveness of the proposed approach is shown through a hypothetical example of an Aircraft Fuel Distribution System (AFDS).

Citation

Gheraibia, Y., Kabir, S., Aslansefat, K., Sorokos, I., & Papadopoulos, Y. (2019). Safety + ai: A novel approach to update safety models using artificial intelligence. IEEE access : practical innovations, open solutions, 7, 135855-135869. https://doi.org/10.1109/ACCESS.2019.2941566

Journal Article Type Article
Acceptance Date Feb 5, 2019
Online Publication Date Sep 16, 2019
Publication Date 2019
Deposit Date Feb 17, 2021
Publicly Available Date Feb 19, 2021
Journal IEEE Access
Print ISSN 2169-3536
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Pages 135855-135869
DOI https://doi.org/10.1109/ACCESS.2019.2941566
Keywords Fault tree; Reliability; Safety modeling; Model repair; Machine learning; Artificial intelligence
Public URL https://hull-repository.worktribe.com/output/2853693

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