基于正交超平形空间定向扩展的滤波故障诊断方法

针对含有未知但有界噪声的离散系统故障诊断问题,提出基于正交超平形空间定向扩展的滤波故障诊断方法.首先,在传统超平形空间的结构基础上,利用相邻时刻超平行空间顶点极值定义正交超平行空间,包裹参数可行集的上下界的同时,保证参数边界值的单调收敛;随后,利用超平行空间与带空间的交集情况,检测系统是否发生故障,进而在故障发生时定向扩展正交超平行空间,依据带空间与正交超平行空间在扩展方向测试集的交集情况实现故障隔离;最后,利用正交超平形空间在迭代过程中的收缩性质完成故障识别.给出的仿真示例验证了算法的可行性和有效性.

Orthometric hyperparallel spatial directional expansion filtering based fault diagnosis method

An orthometric hyperparallel spatial directional expansion filtering based fault diagnosis method is proposed for discrete systems with unknow but bounded noises. Firstly, the orthometric hyperparallel space is defined by the extremum of the parallelotope at the adjacent time based on the spatial structure of traditional parallelotope, to wrap the upper and lower bounds of the feasible parameter set and guarante the monotonic convergence of the parameter boundary value. Then, whether the system is faulty is checked by detecting the intersection of the parallelotopic space and the strip space, and then the orthometric hyperparallel space is extended directionally at the time of fault occurrence, and the fault isolation is realized according to the intersection of the extended orthometric hyperparallel space and the strip space. Finally, the fault identification is completed by the shrinkage property of the orthometric hyperparallel space in the iterative process. The simulations are given to verify the feasibility and effectiveness of the proposed algorithm.