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

Abstract

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

CLC Number:

TP306

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.

Figures/Tables 6

Fig.1

Table 1

Number of times of calculating fault distinguishability between two algorithms"

$α$	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

Table 1

Table 2

Number of calculating fault distinguishability and blocking"

$α$	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

Table 2

Table 3

Optimal solutions of two algorithms when the number of iterations is 500"

$α$	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

Table 3

Table 4

Optimal solutions of two algorithms when the number of iterations is 1250"

$α$	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

Table 4

Table 5

Optimal solutions of two algorithms when the number of iterations is 2000"

$α$	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

Table 5

References 21

1	Hwang I, Kim S, Kim Y, et al. A survey of fault detection, isolation, and reconfiguration methods[J]. IEEE Transactions on Control Systems Technology, 2010, 18(3): 636-653.
2	欧阳丹彤, 刘伯文, 刘梦等. 结合电路结构基于分块的诊断方法[J]. 电子学报, 2018, 46(7): 38-44.
	Dan-tong Ou-yang, Liu Bo-wen, Liu Meng, et al.Diagnostic method based on block based on circuit structure[J]. Acta Electronica Sinica, 2018, 46(7): 38-44.
3	Frisk E, Krysander M, Jung D. A toolbox for analysis and design of model based diagnosis systems for large scale models[J]. IFAC World Congress, 2017, 50(1): 3287-3293.
4	Bhushan M, Narasimhan S, Rengaswamy R. Robust sensor network design for fault diagnosis[J]. Computers Chemical Engineering, 2008, 32(4): 1067-1084.
5	田乃予, 欧阳丹彤, 刘梦等. 基于子集一致性检测的诊断解极小性判定方法[J]. 计算机研究与发展, 2019, 56(7): 1396-1407.
	Tian Nai-yu, Dan-tong Ou-yang, Liu Meng, et al. Diagnostic solution minima determination method based on subset consistency detection[J]. Journal of Computer Research and Development, 2019, 56(7): 1396-1407.
6	Basseville M, Nveniste A, Moustakides B, et al.Optimal sensor location for detecting changes in dynamical behavior[J].[C]∥IEEE Conference on Decision and Control, New Orleans, USA, 1987: 1067-1075.
7	Debouk R, Lafortune S, Teneketzis D. On an optimization problem in sensor selection for failure diagnosis[J].[C]∥IEEE Conference on Decision and Control, New Orleans, USA, 2002: 317-445
8	Wang H, Song Z, Hui W. Statistical process monitoring using improved PCA with optimized sensor locations[J]. Journal of Process Control, 2002, 12(6): 735-744.
9	Frisk E, Krysander M, Slund J. Sensor placement for fault isolation in linear differential-algebraic systems[J]. Automatica, 2009, 45(2): 364-371.
10	Krysander M, Frisk E. Sensor placement for fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2008, 38(6): 1398-1410.
11	Dulmage A L, Mendelsohn N S. Coverings of bipartite graphs[J]. Can J Math, 1958, 10: 517-534.
12	Trave-Massuyes L, Escobet T, Olive X. Diagnosability analysis based on component-supported analytical redundancy relations[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A, Systems and Humans, 2006, 36(5): 1146-1160.
13	Daigle M, Roychoudhury I, Bregon A. Diagnosability-based sensor placement through structural model decomposition[C]∥Second European Conference of the Prognostics and Health Management Society, Nantes, France, 2014: 1-14.
14	Yassine A A, Ploix S, Flaus J M. A method for sensor placement taking into account diagnosability criteria[J]. International Journal of Applied Mathematics and Computer Science, 2008, 18(4): 497-512.
15	Rosich A, Sarrate R, Puig V, et al. Efficient optimal sensor placement for model-based FDI using an incremental algorithm[C]∥IEEE Conference on Decision Control, New Orleans, USA, 2007: 3842-3847.
16	Commault C, Dion J M, Agha S Y.Structural analysis for the sensor location problem in fault detection and isolation[J]. Automatica, 2008, 44(8): 2074-2080.
17	Chamseddine A, Noura H, Raharijaona T, et al.Structural analysis-based sensor location for diagnosis as optimization problem[C]∥IEEE Conference on Decision Control, New Orleans, USA, 2007: 9885815.
18	Sarrate R, Puig V, Esco Be T T, et al. Optimal sensor placement for model-based fault detection and isolation[C]∥IEEE Conference on Decision and Control, New Orleans, USA, 2007: 2723-2728.
19	Akhielarab F N, Estruch R S, Oliva A R. Optimal sensor placement for fuel cell system diagnosis using BILP formulation[C]∥18th Mediterranean Conference on Control and Automation, Marrakech, Morocco, 2010: 1296-1301.
20	Eriksson D, Frisk E, Krysander M.A method for quantitative fault diagnosability analysis of stochastic linear descriptor models[J]. Automatica, 2013, 49(6): 1591-1600.
21	Jung D, Dong Y, Frisk E, et al. Sensor selection for fault diagnosis in uncertain systems[J]. International Journal of Control, 2020, 93(3): 629-639.

Related Articles 15

[1]	Jin-hua WANG,Jia-wei HU,Jie CAO,Tao HUANG. Multi⁃fault diagnosis of rolling bearing based on adaptive variational modal decomposition and integrated extreme learning machine [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 318-328.
[2]	Shao-jiang DONG,Peng ZHU,Xue-wu PEI,Yang LI,Xiao-lin HU. Fault diagnosis of rolling bearing under variable operating conditions based on subdomain adaptation [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 288-295.
[3]	Wei LUO,Bo LU,Fei CHEN,Teng MA. Fault diagnosis method of NC turret based on PSO⁃SVM and time sequence [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 392-399.
[4]	Jie CAO,Jia-lin MA,Dai-lin HUANG,Ping YU. A fault diagnosis method based on multi Markov transition field [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 491-496.
[5]	Fei-yue DENG, LYUHao-yang,Xiao-hui GU,Ru-jiang HAO. Fault diagnosis of high⁃speed train axle bearing based on a lightweight neural network Shuffle⁃SENet [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 474-482.
[6]	Long ZHANG,Tian-peng XU,Chao-bing WANG,Jian-yu YI,Can-zhuang ZHEN. Gearbox fault diagnosis baed on convolutional gated recurrent network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 368-376.
[7]	Xiao⁃lei CHEN,Yong⁃feng SUN,Ce LI,Dong⁃mei LIN. Stable anti⁃noise fault diagnosis of rolling bearing based on CNN⁃BiLSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 296-309.
[8]	Xian-jun DU,Liang-liang JIA. Fault diagnosis of rolling bearing based on optimized stacked denoising auto encoders [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2827-2838.
[9]	Fei CHEN,Zheng YANG,Zhi-cheng ZHANG,Wei LUO. Fault diagnosis method of rotating machinery for unlabeled data [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2514-2522.
[10]	Jie CAO,Zhi-Dong HE,Ping YU,Jin-hua WANG. Bearing fault diagnosis method under unbalanced data distribution [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2523-2531.
[11]	Dan-tong OUYANG,Bi-ge ZHANG,Nai-yu TIAN,Li-ming ZHANG. Fail data reduction algorithm combining configuration checking with local search [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2144-2153.
[12]	Lao-hu YUAN,Dong-shan LIAN,Liang ZHANG,Yi LIU. Fault diagnosis of key mechanical components of aircraft based on densenet and support vector machine [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1635-1641.
[13]	Dan-tong OUYANG,Yang LIU,Jie LIU. Fault diagnosis method based on test set under fault response guidance [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1017-1025.
[14]	Wei LI,Jian CHEN,Shan-yong TAO. Method of enhancing stochastic resonance signal of self⁃adaptive coupled periodic potential system [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1091-1096.
[15]	Feng-wen PAN,Dong-liang GONG,Ying GAO,Ming-wei XU,Bin MA. Fault diagnosis of current sensor based on linearization model of lithium ion battery [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 435-441.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	LI Shoutao, LI Yuanchun. Autonomous Mobile Robot Control Algorithm Based on Hierarchical Fuzzy Behaviors in Unknown Environments[J]. 吉林大学学报(工学版), 2005, 35(04): 391 -397 .
[2]	Li Hong-ying; Shi Wei-guang;Gan Shu-cai. Electromagnetic properties and microwave absorbing property of Z type hexaferrite Ba_3-xLa_xCo₂Fe₂₄O₄₁[J]. 吉林大学学报(工学版), 2006, 36(06): 856 -0860 .
[3]	Zhang Quan-fa，Li Ming-zhe，Sun Gang，Ge Xin . Comparison between flexible and rigid blank-holding in multi-point forming[J]. 吉林大学学报(工学版), 2007, 37(01): 25 -30 .
[4]	Che Xiang-jiu，Liu Da-you，Wang Zheng-xuan . Construction of joining surface with G¹ continuity for two NURBS surfaces[J]. 吉林大学学报(工学版), 2007, 37(04): 838 -841 .
[5]	Liu Han-bing, Jiao Yu-ling, Liang Chun-yu,Qin Wei-jun . Effect of shape function on computing precision in meshless methods[J]. 吉林大学学报(工学版), 2007, 37(03): 715 -0720 .
[6]	Yang Qing-fang, Chen Lin . Division approach of traffic control work zone[J]. 吉林大学学报(工学版), 2006, 36(增刊2): 139 -142 .
[7]	Yu Hua-nan， Kang Jian . An improved blind multiuser detection scheme based on Kalman filter[J]. 吉林大学学报(工学版), 2006, 36(增刊2): 122 -125 .
[8]	Li Yue-ying，Liu Yong-bing，Chen Hua . Surface hardening and tribological properties of a cam materials[J]. 吉林大学学报(工学版), 2007, 37(05): 1064 -1068 .
[9]	Zhang He-sheng, Zhang Yi, Wen Hui-min, Hu Dong-cheng . Estimation approaches of average link travel time using GPS data[J]. 吉林大学学报(工学版), 2007, 37(03): 533 -0537 .
[10]	Qu Zhao-wei，Chen Hong-yan，Li Zhi-hui，Hu Hong-yu，Wei Wei . 2D view reconstruction method based on single calibration pattern [J]. 吉林大学学报(工学版), 2007, 37(05): 1159 -1163 .

Set blocking⁃based approach to sensor selection in uncertain systems

RICH HTML

PDF (PC)