基于特征参数分布的机载电子设备故障诊断与预测

Feature Parameter Distribution Based Diagnostics and Prognostics of Aviatic Electronics

基于特征参数趋势进化的故障预测是一种有效的方法,引入了一种考虑特征参数概率分布的新型判据进行多故障模式诊断与预测.基于过程神经网络建立了高精度预测模型,根据模型和部件使用记录进行趋势预测.基于方法对机载电子设备进行案例研究,结果表明,方法的判定结果更加符合多故障模式并存、故障严重程度不同的实际情况,而具有较高拟和、泛化预测精度的PNN模型是一种有效的趋势预测方法.

Prognostics based on feature parameter trend evolution is an effective method, a new fault judgement integrated with feature parameter distribution is introduced to implement multi-modes diagnostics and prognostics. A more precise forecast model is built based on procedure neural network, and trend forecast is performed based on both the model and usage record of feature parameters. The way researched above is applied in a control-transfer component to set an research example. Researchs show that results aquired by this method are more according with the actual situation in which there are more than one fault modes that are of different severity, and PNN model is an effective trend forecast method as it＇s of higher accuracy in both approximating and extended forecasting.