| Citation: | MA Jiali, CHEN Guo, KANG Yuxiang, et al. Multi-objective fusion diagnosis of aeroengine wear failure[J]. Journal of Aerospace Power, 2024, 39（10）：20220191 doi: 10.13224/j.cnki.jasp.20220191
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According to the characteristics of various oil analysis data, an aeroengine wear fault fusion diagnosis method was established to realize comprehensive evaluation of aeroengine wear state based on oil analysis data. The fault fusion diagnosis method included wear fault qualitative analysis, location analysis and cause analysis. Taking the original analysis data of spectrum, Ferrography and particle count as the input, the qualitative diagnosis results of engine wear fault were obtained based on D-S evidence theory through qualitative analysis; in the location analysis, a rolling bearing fault location identification model based on deep learning was established, and the original data of energy spectrum analysis were used as the model input to realize the intelligent identification of aeroengine wear location; finally, in the cause analysis, using the qualitative results and positioning results, according to the experience of domain experts, the knowledge rules based on if-then were established to find out the cause of engine wear fault. The effectiveness and reliability of the proposed method were verified by using the actual oil monitoring data, the diagnostic accuracy can reach up to 100%, and the results fully showed the correctness and effectiveness of the method.
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