【摘要】 针对航空发动机工作环境和结构的复杂性,设计了一种基于人工神经网络的航空发动机故障诊断方法。使用BP神经网络实现对航空发动机故障的诊断和识别,为了加快BP算法的收敛速度,采用带惯性项的批处理BP算法对BP神经网络进行训练。通过对检验样本的测试验证了该方法的有效性和可行性,将不同隐含层数及不同误差精度的算法性能进行了比较分析,结果表明本设计的网络结构及选取的误差精度能满足实际需要。更多还原

【Abstract】 In consideration of the complexity of aero-engine work condition and structure,an aero-engine fault diagnosis method based on artificial neural network is designed in this paper.The BP neural network is used in this method to realize the aero-engine fault diagnosis and recognization.The BP neural network was trained by batch processing BP algorithm with inertia item to increase the convergence speed of BP algorithm.The testing of samples under test were done to prove the validity and feasibility of the method.The performance of algorithms with different network structure and error was compared and analyzed.The result shows that the network structure designed and error selected in this paper can satisfy actual requirement.更多还原