基于极限学习机的航空发动机传感器故障诊断

Aircraft engine sensor fault diagnosis based on extreme learning machine

针对当前应用于航空发动机传感器故障诊断中的基于梯度的传统学习算法多存在参数选择困难、容易陷入局部最小化、过拟合等问题,提出了基于极限学习机(ELM)的航空发动机传感器故障诊断方法。算法只需设置隐含层神经元的个数,能够较好地避免上述问题,缩短故障诊断时间、提升诊断精度。通过仿真试验表明:基于ELM算法所建的航空发动机传感器故障诊断模型要比基于BP神经网络算法所建的模型耗时短且精度高。

Aiming at problems of traditional gradient-based learning algorithm used for aircraft engine sensor fault diagnosis always have currently, such as difficulties with multi-parameter selection, easy to fall into local minimum,over-fitting, and so on, propose fault diagnosis method for aircraft engine sensor based on extreme learning machine（ELM）. ELM algorithm can avoid above problems,and further reduce fault diagnostic time and improve diagnostic precision, since it only need to set a parameter,i, e. the number of hidden layer. Simulation test shows that aircraft engine sensor fault diagnosis model based on ELM algorithm has higher precision and shorter time than that based on BP neural network algorithm.