CAJ | 학술논문

在飞行试验中，由于涡喷发动机遥测数据有限，因此要对其故障进行准确分析定位存在很大难度。利用小波变换对涡喷发动机涡轮转速、喷嘴前压力等遥测数据经过去噪提取后，找出奇异点，并经系统去噪筛选，形成数据文件，利用BP神经网络的模式分类功能，发现故障出现的时刻及部件，并结合研制过程中地面试车数据库，可实现准确定位涡喷发动机故障部位及类型。实际应用证明方法实用有效，可进一步推广到其他系统或部件的故障分析工作中。
재비행시험중，유우와분발동궤요측수거유한，인차요대기고장진행준학분석정위존재흔대난도。이용소파변환대와분발동궤와륜전속、분취전압력등요측수거경과거조제취후，조출기이점，병경계통거조사선，형성수거문건，이용BP신경망락적모식분류공능，발현고장출현적시각급부건，병결합연제과정중지면시차수거고，가실현준학정위와분발동궤고장부위급류형。실제응용증명방법실용유효，가진일보추엄도기타계통혹부건적고장분석공작중。
Due to the limited telemetry data for the turbojet engine during the flight test, it is difficult to locate accurately the turbojet engine’s fault. After the de-noising extraction with wavelet transform of the data, such as speed of the turbojet engine, former pressure of nozzle etc, the singularity is found and the data file is formed through the system noise filtering. Using the BP network pattern classification function, the moment of failure oc?curs and default parts are detected. With the ground test database in the development process, it can locate accu?rately the turbojet engine defaults parts and types. The results show that the method is effective and can be applied in other subsystem fault analysis.