摘要
含有噪声的、正常和稳定的传感器数据训练 ART2神经元网络 ,用于液体火箭发动机( L RE)故障检测。每个传感器连续窗的功率谱输入 ART2神经元网络进行学习 ,试验学习好的神经网络 ,验证其能否有效地检测出发动机故障以及故障发生时间。传感器数据来自某变推力液体火箭发动机地面试车 RS61。试验结果表明 。
An ART2 (Adaptive Resonance Theory) neural network was trained to detect failures in a liquid rocket engine (LRE) from noisy normal steady-state sensor data.Power spectra of successive windows of individual sensor data were presented to an ART2 neural network to learn.The trained network was then tested to verify its effectiveness of failure detection and failure onset detection.Sensor data were collected from ground tests RS61 of the variable thrust LRE.The test results show that detected by the neural network corresponds with that the failure onset determined by experts from their post-test analyses.
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
1993年第4期403-405,共3页
Journal of Aerospace Power
基金
国家自然科学基金资助项目
