基于正多胞体线性规划的滤波故障诊断方法

针对含有未知但有界噪声的线性系统故障诊断问题,提出一种基于正多胞体的滤波故障诊断方法,利用线性规划方程表示递归运算过程中的约束条件,同时在递归过程中更新正多胞体的空间表达式,求取每个参数的不确定区间,并以正多胞体的空间形态描述参数可行集,通过检测滤波器参数正多胞体可行集是否为空,判断系统有无故障.针对不同故障类型设计集员滤波器,当参数正多胞体可行集为空,即系统发生故障时,采用模型匹配的方式实现故障诊断,如果出现故障样本中未包含的故障类型,则将该故障类型添加到故障样本库中.分别给出低维和高维空间的仿真实例,描述正多胞体空间的结构变化情况,给出不同故障状态下的正多胞体空间分析结果.最后通过仿真结果和分析验证了所提出的故障诊断方法的有效性和实用性.

Orthotopic linear programming filtering based fault diagnosis method

An orthotopic linear programming filtering based fault diagnosis method is proposed for the linear systems with unknown but bounded noises. The linear programming equation is used to show the constraints in the recursive operation process and the space expression of the orthotopic is updated during the recursive computation. Each parameter has its own uncertainty interval and the feasible parameter set is described by the orthotopic, with checking whether the filter of orthotopic feasible parameter set is empty or not to finish fault detection. Set membership filters are designed according to different faults, when the orthotopic feasible parameter set is empty, which means as the fault occurred, the model is matched to achieve fault identification. If a fault type is not included in the fault sample, the type of fault is added to the fault sample base. The low dimension and high dimension examples are given to illustrate the performance of the orthotopices and to analysis the space expressions under different fault situations. The simulation results and analysis verify the effectiveness and practicability of the proposed fault diagnosis method.