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A Knowledge Graph-Based Analysis Framework for Aircraft Configuration Change Propagation

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Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1682))

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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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References

  1. Zhenhua, S.: Research on configuration management for aircraft design. Intell. Manuf. 04, 50–53 (2020)

    Google Scholar 

  2. Chen, X., Jia, S., Xiang, Y.: A review: Knowledge reasoning over knowledge graph. Expert Syst. Appl. 141, 112948 (2020)

    Article  Google Scholar 

  3. Zhongwei, G., Rong, M., Haicheng, Y., et al.: Engineering Change Based on Product Development Network Hub Node [J]. Comput. Integr. Manuf. Syst. 18(1), 40–46 (2012)

    Google Scholar 

  4. Zhang, N., Yang, Y., Wang, J., et al.: Identifying core parts in complex mechanical product for change management and sustainable design. Sustainability 10(12), 4480 (2018)

    Article  Google Scholar 

  5. Xi, Y., Yimin, D., Peng, Y.: Design change propagation process and characteristics analysis of variable function machinery based on FBS. J. Eng. Des. 23(1), 8–13 (2016)

    Google Scholar 

  6. Yupeng, L., Xiaochun, W., Xiaolin, L.: Impact assessment of complex product design changes based on BBV network model[J]. Comput. Integr. Manuf. Syst. 7, 1429–1438 (2017)

    Google Scholar 

  7. Hamraz, B., Caldwell, N.H.M., Ridgman, T.W., et al.: FBS Linkage ontology and tech-nique to support engineering change management[J]. Res. Eng. de-sign 26(1), 3–35 (2015)

    Article  Google Scholar 

  8. Lu, G., Zhang, L., Jin, M., Li, P., Huang, X.: Entity alignment via knowledge embedding and type matching constraints for knowledge graph inference. J. Amb. Intell. Hum. Comput. 13, 5199–5209 (2021)

    Google Scholar 

  9. Chao L, Wang T, Chu W. PIE: a parameter and inference efficient solution for large scale knowledge graph embedding reasoning. arXiv preprint arXiv:2204.13957, 2022

  10. Cheng, K., Yang, Z., Zhang, M., et al.: UniKER: a unified framework for combining embedding and definite horn rule reasoning for knowledge graph inference. In: Proceedings of the. Conference on Empirical Methods in Natural Language Processing 2021, pp. 9753–9771 (2021)

    Google Scholar 

  11. Chen Y. Convolutional Neural Network for Sentence Classification. University of Waterloo (2015)

    Google Scholar 

  12. Chen, T., Xu, R., He, Y., et al.: Improving sentiment analysis via sentence type classi-fication using BiLSTM-CRF and CNN[J]. Expert Syst. Appl. 72, 221–230 (2017)

    Article  Google Scholar 

  13. Grover, A., Leskovec, J.: node2vec: Scalable feature learning for net-works. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2015)

    Google Scholar 

  14. Mor, B., Garhwal, S., Kumar, A.: A systematic review of hidden markov models and their applications[J]. Arch. Comput. Methods Eng. 28(3), 1429–1448 (2021)

    Article  MathSciNet  Google Scholar 

  15. Pearl, J., Mackenzie, D.: The Book of Why: The New Science of Cause and Effect. Basic Books (2018)

    Google Scholar 

  16. Zepeng, S.: Research on change impact assessment process based on CM2. Mech. Eng. 4, 110–112 (2020)

    Google Scholar 

  17. Congdong, L., Zhiwei, Z., Cejun, C., et al.: Impact Assessment of Engineering Change Propagation for Complex Products Based on Multiple Networks. J. Comput. Appl. 40(4), 1215 (2020)

    Google Scholar 

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Correspondence to Hongming Cai .

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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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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2384-7

  • Online ISBN: 978-981-99-2385-4

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