涡轮泵实时故障检测的改进自适应相关阈值算法

1引言火箭发动机在飞行使用之前,需要分析试车振动数据以评估发动机的性能与状态1]。涡轮泵是液体火箭发动机中最复杂故障概率最高的部件2],而振动是涡轮泵故障的重要起因3],因此,涡轮泵振动数据的分析显得尤为重要。目前,它的主要分析方法有时域统计4]、频谱分析5]、神经

Improved adaptive correlation thresholds algorithm for turbopump real-time fault detection

In order to detect the Liquid Rocket Engine(LRE) turbopump faults in real time and effectively,and avoid estimating the thresholds of same statistical features for all vibration test parameters respectively and confirm their fault detection logics,the Improved Adaptive Correlation Thresholds Algorithm(IACTA) was presented in this paper.At first,the theory of Adaptive Correlation thresholds Algorithm(ACA) was discussed.And then three vibration parameters and three statistical features,which are weak linear correlation and well reflect the turbopump faults characteristic,were selected.At the same time,the thresholds adaptive estimation(the means and standard deviations adaptive estimation) and improvement(bilateral thresholds was improved to unilateral ones) methods for Mahalanobis distance square of these features were researched,and the thresholds refreshment without fault data were investigated.Under error probability 0.025,the IACTA fault detection real time capability was validated with 8 real time data from rotor rub-impact test;and its fault detection validity was validated with 24 historical data from LRE test and 196 data from rotor rub-impact test.In this case,it is shown that IACTA mean real-time fault detection time is 226.625ms,while its fault detection probability achieves 96.0%.