Yes / 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). / DEIS H2020 Project under Grant 732242
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/17422 |
| Date | 16 September 2019 |
| Creators | Gheraibia, Y., Kabir, Sohag, Aslansefat, K., Sorokos, I., Papadopoulos, Y. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ |
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