基于修正高维云的故障映射模型

故障预测需要完成从状态空间到趋势空间再到故障空间的两个映射过程。为使后一个映射结果更为准确,在借鉴多元正态分布理论的基础上,提出了一种基于修正高维云的故障映射模型。采用能够描述论域相关程度的协熵和协超熵来代替原来的熵和超熵,从而更好地解决了多论域之间的相关性问题。利用修正高维云发生器组成的多规则推理系统建立故障映射模型,将趋势预测数据准确映射到故障空间,得到最终故障预测结果。为验证方法的有效性,针对飞机操纵面损伤故障进行了仿真和分析,仿真结果充分表明所提出的模型具有较高的映射精度和效率。

Fault mapping model based on correction high dimensional cloud

Fault prediction needs to complete two mapping processes, one is from state space to trend space, and the other is from trend space to fault space. To make the latter mapping results more accurately, a fault mapping model based on correction high dimensional cloud is proposed on the basis of multivariate normal distribution theory. Entropy and hyper entropy are replaced by covariance entropy and covariance hyper entropy in the correction cloud model, which are used to describe the correlation of some domains. The fault mapping model is constructed by the multi-rule-based reasoning system composed of the correction high dimensional cloud generators. Finally, the trend prediction values are mapped to the fault space accurately to generate the final prediction results. In order to verify the validity of the model, the simulation and analysis of rudder damage are performed. The results demonstrate the proposed model has high mapping precision and efficiency.