基于集合阻塞的不确定系统中传感器选择方法

Set blocking-based approach to sensor selection in uncertain systems

动态系统故障检测与隔离(FDI)的一个重要步骤是选择一组满足故障检测性与故障隔离性且花费最小的传感器集合。针对不确定系统,故障可诊断性量化方法被用来量化系统的故障检测性和故障隔离性。由于传感器选择问题的搜索空间随传感器规模增大呈指数增长,目前没有研究针对量化可诊断性的不确定系统提出传感器选择的完备方法。针对该问题本文提出基于集合阻塞策略的传感器选择算法:通过二进制整数优化问题(BILP)实现子集阻塞与超集阻塞;通过迭代阻塞搜索空间减小所需遍历的节点。在标准测试用例上的实验结果表明:针对实验中的绝大多数搜索空间,与深度优先遍历方法相比,本文方法效率提高了4.41~103.37倍。且在区分度计算次数相同的情况下,本文方法得到的大多数解优于现行的高效算法。

A significant step of fault detection and isolation(FDI) in dynamic systems is to select a set of sensors that meets the fault detectability and isolability with the least cost. For uncertain systems, the quantification method of fault distinguishability is used to quantify the fault diagnosability performance.Since the search space of the sensor selection problem increases exponentially with the increase of the sensor scale, there is currently no research on complete method of sensor selection for quantifying the fault diagnosability in uncertain systems. This paper proposes a sensor selection algorithm based on the set blocking strategy to solve this problem: realizing subset blocking and superset blocking through BILP;reducing the nodes that need to be traversed through iterative blocking search space. The experimental results show that compared with the depth-first traversal method, the efficiency of the proposed method is increased by 4.41~103.37 times for most of the use cases in the experiment.