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.