Abstract
Configuration change management is an important part of the aircraft operation. Accurate and reliable analysis of the configuration status will directly affect the airworthiness of the aircraft. For the huge amount of complicated configuration data, precisely prediction of change influence scope by experience-based analysis becomes a challenge. Besides, it is still lack of a theoretically complete solution for the prediction of change propagation result from the simultaneous disturbance of multi-factors. Therefore, we propose a knowledge graph-based analysis framework for aircraft configuration change propagation, which solve the prediction problem of potential influence scope under multi-factor change. Firstly, the initial configuration items are identified according to the input change instructions, and the influence probability network of configuration items based on graph representation is constructed. Secondly, two causal pair algorithms are designed and adopted: the single factor causal pair algorithm based on Markov chain and the multi-factor causal pair algorithm based on intervention, which outputs accurate and complete change influence scope and interpretable influence propagation graph. A specific case is analyzed to verify the feasibility of the framework. Finally, We compare our framework with relevant works. The results show that the proposed method effectively solves the problem of assessing the influence range of multiple factors in configuration change, with integrity, accuracy, scalability and interpretability.
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Wang, Y., Zhang, X., Cai, H., Wan, B., Liu, M., Jiang, L. (2023). A Knowledge Graph-Based Analysis Framework for Aircraft Configuration Change Propagation. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1682. Springer, Singapore. https://doi.org/10.1007/978-981-99-2385-4_41
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DOI: https://doi.org/10.1007/978-981-99-2385-4_41
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