CAJ | 학술논문

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。
침대당전응용우항공발동궤전감기고장진단중적기우제도적전통학습산법다존재삼수선택곤난、용역함입국부최소화、과의합등문제，제출료기우겁한학습궤（ ELM）적항공발동궤전감기고장진단방법。산법지수설치은함층신경원적개수，능구교호지피면상술문제，축단고장진단시간、제승진단정도。통과방진시험표명：기우ELM산법소건적항공발동궤전감기고장진단모형요비기우BP신경망락산법소건적모형모시단차정도고。
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.