变工况下航空逆变器健康评估方法研究

工况的变化会引起功率变换器电路健康表征参数随之变化，导致无法判断健康表征参数是因电路性能的退化还是因工况的变化引起的。针对该关键问题，以航空逆变器为研究对象，首先采用多评价指标优选模型优选出相关敏感的健康表征参数；然后基于极限学习机建立工况-无故障情况下健康表征参数映射模型；最后基于当前健康表征参数与映射模型输出的健康表征参数之间的相对变化量构建考虑工况条件的电路健康指标，实现不同工况下航空逆变器的健康评估。实验结果表明，该评估方法可以有效减小工况变化对健康指标的影响。在变工况情况下，相比于直接基于欧氏距离构建健康指标的评估方法，平均绝对误差（Mean Absolute Error，MAE）和均方根误差（Root-Mean-Square Error，RMSE）分别降低了64.4%、66.8%。

Research on health evaluation method of aviation inverter under variable working conditions

The variation of operating conditions will lead to the variation of circuit health characterization parameters, so it is impossible to judge whether the health characterization parameters are caused by the degradation of circuit performance or the variation of operating conditions. Aiming at this key problem, aviation inverter is taken as the research object. Firstly, a multi-evaluation index optimization model was used to select the relevant sensitive health characterization parameters. Then, based on extreme learning machine, the mapping model of health representation parameters under the condition of working condition and no fault was established. Finally, based on the relative changes between the current health representation parameters and the health representation parameters output by the mapping model, the circuit health indicators considering working conditions were constructed to realize the health assessment of the aviation inverter under different working conditions. The experimental results show that the proposed method can effectively reduce the influence of working conditions on health indicators, and the MAE and RMSE of the proposed method are 64.4% and 66.8% lower than those of the method directly based on Euclide distance.