# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 4 Conclusions  
This paper proposes a multi-source information fusion diagnosis method for aero engine fault detection. By integrating diagnostic information from gas path and vibration subsystems at the decision level and validating this through calculations and an analysis based on real operational data from a two-shaft aero engine, the study demonstrates the effectiveness and applicability of the proposed method. The main conclusions are as follows:  
1. This study builds a decision-level fusion framework integrating diagnostic features independently extracted from gas path, vibration, and lubrication subsystems. A GPA and vibration signal analysis are employed for fault characterization. Preliminary decisions are made based on subsystem-specific criteria, enabling effective fusion across heterogeneous data sources;
2. A two-layer Bayesian network is developed to capture dependencies between fault features and component-level failures. Without prior knowledge, it achieves a diagnostic confidence of 87.2%, increasing to 91.2% with prior knowledge. While Bayesian fusion improves diagnostic reliability, its application is constrained by the need for accurate prior knowledge and complex CPT construction;
3. To mitigate the dependence on prior knowledge, Dempster-Shafer (D-S) evidence theory is introduced for decision fusion. By incorporating Gaussian-type membership functions, the D-S-based method effectively handles uncertainty caused by sensor signal fluctuations. The fusion result achieves a diagnostic confidence of 99.6%, significantly reducing volatility in fault probability trends and demonstrating superior robustness and early fault detection capability compared to Bayesian-based fusion. By utilizing real engine operational data, the proposed method achieves a high diagnostic confidence level and demonstrates good adaptability to practical aero engine fault diagnosis tasks. In future work, the fusion framework can be extended by incorporating additional diagnostic sources, such as lubrication system monitoring, to further enhance diagnostic coverage and accuracy. Figure 8: Fusion diagnosis results based on Bayesian network. **Author Contributions:** Conceptualization, K.Y., Y.S. and H.Z. ; Methodology, K.Y., Y.S. and Y.C. ; Software, K.Y., Y.S. and Y.C. ; Investigation, Y.C. and H.Z. ; Writing--original draft preparation, K.Y. and Y.S. ; Writing--review and editing, Y.C. and H.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data are not publicly available due to privacy and ethical restrictions. Requests for access to the data should be directed to the corresponding author at [EMAIL_ADDRESS]. **Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company AECC Commercial Aircraft Engine Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.