航空发动机磨损故障多目标融合诊断

Multi-objective fusion diagnosis of aeroengine wear failure

针对多种油液分析数据的特点,建立了航空发动机磨损故障融合诊断方法,实现基于油液分析数据的航空发动机磨损状态综合评估。该故障融合诊断方法包括磨损故障定性分析、定位分析和定因分析。定性分析以光谱、铁谱和颗粒计数原始分析数据为输入,基于(Dempster-Shafer)证据理论获得发动机磨损故障定性诊断结果;在定位分析部分,建立了基于深度学习的滚动轴承故障部位识别模型,以能谱分析原始数据作为模型输入,实现了航空发动机磨损部位的智能识别;最后,在定性分析部分,利用定性结果和定位结果,根据领域专家的经验,建立了基于if-then的知识规则,找出发动机磨损故障原因;利用实际油液监测数据对所提方法的有效性和可靠性进行验证,诊断精度最高可达到100%,结果充分表明了该方法的正确性、有效性。

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.