中文摘要：

针对自适应相关算法（ACA）采用单一理论阈值,且马氏（Mahalanobis）距离的修正方法不太合理的问题,在已有的自适应相关方法基础上,提出了改进的火箭发动机实时故障检测的自适应相关算法。改进之处有：1）利用理论阈值和发动机历史数据统计得到的经验阈值共同作为判断依据;2）提出了修正Mahalanobis距离的计算方法,去除偏离数据均值最大的1～3个参数对Mahalanobis距离的贡献。在给定的5%误检率下,通过发动机的仿真数据验证了改进的算法能对发动机稳态工作过程中的故障及时准确地检测,并能有效解决野值存在时的误报警情况。

英文摘要：

Aiming at the problems that the Adaptive Correlation Algorithm（ACA） uses single theoretical threshold and the Mahalanobis distance correction method is unreasonable,we proprose an improved adaptive correlation algorithm for real-time rocket engine fault detection on the basis of existing research.The improvements are： 1） The empirical threshold obtained from the historical data of the engine is used together with the theoretical threshold as judgment criterion; and 2） Mahalanobis distance is corrected by removing 1to 3 parameters that contribute the largest deviation to data mean.Under the given false detecting rate of5%,simulation data demonstrate that the improved algorithm can give timely and accurate fault detection result,and can effectively solve false alarm problem when outliers exist.