Abstract
This study proposed a novel Bayesian Network model integrating Graph Attention mechanism and Adaptive Prior algorithm, termed GAT-BN, to address the challenges of data sparsity and imbalanced fault distribution in root cause analysis (RCA) of aircraft engines. The model incorporated domain-specific hierarchical constraints during the structure learning phase, ensuring that the derived network topology aligns with the physical fault propagation logic. For parameter learning, association rules mined from global data serve as robust prior knowledge. A hierarchical attention mechanism was designed to adaptively calibrate the prior strength. This mechanism effectively compensated for the scarcity of deep fault data. Moreover, the embedded GAT module autonomously learns node criticality scores, generating a neural-based prior distribution that enables the model to focus on low-frequency yet high-consequence root causes while mitigating the interference of high-frequency but low-criticality faults. Extensive evaluations on a dataset of 634 engine fault records, using Top-K hit rate and mean absolute error as metrics, demonstrate that GAT-BN performs better than traditional Bayesian Networks and other benchmark models under the experimental settings. It exhibits exceptional robustness under data-sparse scenarios. The proposed framework presents a new paradigm of structure-knowledge-data collaborative driving for intelligent fault diagnosis in complex industrial systems.
Data availability
The data that support the findings of this study are available from the Corresponding Author, Peng Dong, upon reasonable request.
References
Liu, X. & An, S. Failure propagation analysis of aircraft engine systems based on complex network. Procedia Eng. 80, 506–521 (2014).
Heidenberger, P. et al. Aircraft engine fault diagnosis based on fused convolutional transformer. J. Appl. Artif. Intell. 1 (2), 218–231 (2024).
Zhao, J. et al. Root cause analysis of a cracked primary heat exchanger in a gas wall-mounted boiler. Eng. Fail. Anal. 153, 107583 (2023).
Mbogu, H. M. & Nicholson, C. D. Data-driven root cause analysis via causal discovery using time-to-event data. Comput. Ind. Eng. 190, 109974 (2024).
Hua, J. et al. A zero-shot prediction method based on causal inference under non-stationary manufacturing environments for complex manufacturing systems. Robot. Comput. Integr. Manuf. 77, 102356 (2022).
Amin, M. T. et al. A data-driven bayesian network learning method for process fault diagnosis. Process Saf. Environ. Prot. 150, 110–122 (2021).
Yuan, L. et al. Improved Root Cause Analysis Based on Quality Function Deployment-Decision-Making Trial and Evaluation Laboratory for Complex System Faults. 2023 14th International Conference on Reliability, Maintainability and Safety (ICRMS), Urumuqi, China, :328–333. (2023).
de Pater, I., Reijns, A. & Mitici, M. Alarm-based predictive maintenance scheduling for aircraft engines with imperfect remaining useful life prognostics. Reliab. Eng. Syst. Saf. 221, 108341 (2022).
Liu, L., Song, X. & Zhou, Z. Aircraft engine remaining useful life Estimation via a double attention-based data-driven architecture. Reliab. Eng. Syst. Saf. 221, 108330 (2022).
Wang, K., Liu, C. & Lu, Y. Ensemble bayesian network for root cause analysis of product defects via learning from historical production data. J. Manuf. Syst. 75, 102–115 (2024) .
Agrawal, R., Imieliński, T. & Swami, A. Mining association rules between sets of items in large databases[C]//Proceedings of the 1993 ACM SIGMOD international conference on Management of data. : 207–216. (1993).
Gallo, M. D. et al. A self-learning framework combining association rules and mathematical models to solve production scheduling programs. Prod. Manuf. Res. 12(1), 2332285 (2024).
Özaydın, E. et al. A hybrid model for marine accident analysis based on bayesian network (BN) and association rule mining (ARM). Ocean Eng. 247, 110705 (2022).
Khosbayar, A., Valluru, J. & Huang, B. Adaptive inference for bayesian network soft-sensor in the presence of process and sensor drift. Can. J. Chem. Eng. 100(9), 2119–2134 (2022).
Li, X. et al. A physically inspired and bounded metric learning framework for fault detection and multi-class classification in mechanical systems. Expert Syst. Appl. 302, 130393 (2026).
Yang, Y. et al. Graph attention U-Net to fuse multi-sensor signals for long-tailed distribution fault diagnosis. Eng. Appl. Artif. Intell. 126, 106927 (2023).
Liu, Y. & Jafarpour, B. Graph attention network with Granger causality map for fault detection and root cause diagnosis. Comput. Chem. Eng. 180, 108453 (2024).
Luo, J. et al. Causal Temporal graph attention network for fault diagnosis of chemical processes. Chin. J. Chem. Eng. 70, 20–32 (2024).
Xu, Q., Pang, Y. & Liu, Y. Air traffic density prediction using bayesian ensemble graph attention network (BEGAN). Transp. Res. Part. C: Emerg. Technol. 153, 104225 (2023).
Huang, Y. et al. Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine. Energy 283, 129120 (2023).
Lu, S. et al. Remaining useful life prediction via interactive Attention-Based deep Spatio-Temporal network fusing multisource information. IEEE Trans. Industr. Electron. 71 (7), 8007–8016 (2024).
Li, X. et al. Causal graph inference with adaptive dynamic structure learning for mechanism-oriented fault diagnosis in dynamic industrial systems. Reliab. Eng. Syst. Saf. 266(Part B), 111865 (2026).
Liu, C. et al. FERN: leveraging graph attention networks for failure evaluation and robust network design. IEEE/ACM Trans. Netw. 32(2), 1003–1018 (2024).
Kulkarni, C. S., Corbetta, M. & Robinson, E. Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks. Annual Conference of the PHM Society,12(1):12. (2020).
Wang, X. et al. Risk propagation analysis of domino effect in chemical accident: an integrated approach with data mining and bayesian networks. J. Loss Prev. Process. Industries. 98, 105745 (2025).
Liu, N. et al. Fault detection and diagnosis using bayesian network model combining mechanism correlation analysis and process data: application to unmonitored root cause variables type faults. Process Saf. Environ. Prot. 164, 15–29 (2022).
Jospin, L. V. et al. Hands-On bayesian neural Networks—A tutorial for deep learning users. IEEE Comput. Intell. Magazine. 17(2), 29–48 (2022).
Cao, Y. et al. Fault detection of complicated processes based on an enhanced transformer network with graph attention mechanism. Process Saf. Environ. Prot. 186, 783–797 (2024).
Acknowledgements
This study was supported by the National Social Science Foundation of China (Grant No. 2024-SKJJ-C-027) and the Naval University of Engineering independent research projects (No. 2025500330).
Funding
This study was funded by the National Social Science Foundation of China (Grant No. 2024-SKJJ-C-027) and the Naval University of Engineering independent research projects (No. 2025500330).
Author information
Authors and Affiliations
Contributions
Conceptualization, L.Y., G.H. and P.D.; Methodology, L.Y., G.H.; Validation, L.Y., G.H., and P.D.; Data curation and Finding the Cause of Failure, G.H.; Writing—original draft, L.Y., G.H.; supervision, P.D.; Funding acquisition, L.Y., G.H. All authors reviewed and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Declaration of competing interest
The authors declare that they have no conflicts of interest to report regarding the present study.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Yuan, L., Han, G. & Dong, P. Improved bayesian network with graph attention and prior algorithm for aircraft engine fault root cause analysis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36883-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-36883-7
