基于集合阻塞的不确定系统中传感器选择方法

doi:10.13229/j.cnki.jdxbgxb20210694

摘要/Abstract

摘要：

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。

关键词: 故障诊断, 传感器选择, 基于模型诊断, 故障可诊断性分析

Abstract:

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.

Key words: fault diagnosis, sensor selection, model-based diagnosis, fault diagnosability analysis

中图分类号:

TP306

欧阳丹彤,孙睿,田新亮,高博涵. 基于集合阻塞的不确定系统中传感器选择方法[J]. 吉林大学学报(工学版), 2023, 53(2): 547-554.

Dan-tong OU-YANG,Rui SUN,Xin-liang TIAN,Bo-han GAO. Set blocking⁃based approach to sensor selection in uncertain systems[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 547-554.

图/表 6

图1

表1

基于集合阻塞的传感器选择算法与深度优先遍历的故障区分性计算次数"

$α$	Block-based	Depth First	Increase
0.9	529	411	-0.22
0.8	573	3 101	4.41
0.7	594	10 001	15.84
0.6	1 321	21 799	15.50
0.5	3 078	38 379	11.47
0.4	614	57 954	93.39
0.3	1 075	77 772	71.35
0.2	1 125	97 593	95.75
0.1	1 125	117 414	103.37

表1

表2

故障区分性计算次数与阻塞次数"

$α$	Judging times	Number of blocking	ratio
0.9	529	92	5.75
0.8	573	104	5.51
0.7	594	114	5.21
0.6	1321	262	5.04
0.5	3078	593	5.19
0.4	614	110	5.58
0.3	1075	185	5.81
0.2	1125	190	5.92
0.1	1125	190	5.92

表2

表3

迭代次数为500时两个算法的最优解"

$α$	Block-based	GSA
0.9	14.7	14.73
0.8	11.9	12.11
0.7	9.1	9.24
0.6	8.2	8.21
0.5	6.9	7.06
0.4	4.8	5.34
0.3	5.1	4.93
0.2	4.8	5.27
0.1	4.8	5.18

表3

表4

迭代次数为1250时两个算法的最优解"

$α$	Block-based	GSA
0.9	14.3	14.39
0.8	11.2	11.87
0.7	8.8	8.79
0.6	7.5	7.83
0.5	6.9	6.93
0.4	4.8	5.10
0.3	4.9	4.97
0.2	4.8	5.02
0.1	4.8	5.04

表4

表5

迭代次数为2000时两个算法的最优解"

$α$	Block-based	GSA
0.9	14.1	14.24
0.8	11.2	11.52
0.7	8.6	8.50
0.6	7.5	7.64
0.5	6.7	6.74
0.4	4.8	4.99
0.3	4.8	4.90
0.2	4.8	4.98
0.1	4.8	4.95

表5

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