Skip to main content

Advertisement

SpringerLink
Log in

Optimal Sensor Placement Considering Both Sensor Faults Under Uncertainty and Sensor Clustering for Vibration-Based Damage Detection

  • Research Paper
  • Published:
Structural and Multidisciplinary Optimization Aims and scope Submit manuscript

Abstract

Use of a sensor network to provide adequate and reliable information is paramount for accurate damage detection of structures. However, unavoidably, deployed sensors are occasionally subject to failure faults, which, in turn, cause missing information. Placement of multiple backup sensors in a local region could overcome this difficulty and increase the sensor redundancy; however, this approach leads to a sensor clustering problem and higher costs in sensor deployment. Further, model uncertainty is another important issue that should be considered in a sensor network design. Accordingly, this work is dedicated to presenting a framework for optimization of sensor distribution that considers both sensor faults under uncertainty and sensor clustering for vibration-based damage detection. Based on the effective independence method, the first design objective is newly formulated to consider sensor faults under uncertainty. Moreover, a novel index that is universally applicable for any type of structure is proposed to evaluate sensor clustering, which is treated as the second objective. The non-dominated sorting genetic algorithm II is adopted to solve this multi-objective optimization problem, and Monte Carlo simulation (MCS) is employed for uncertainty analysis in the first objective. To reduce computation costs, real performance evaluations in MCS are replaced with Gaussian process regression models. Based on the vibration information achieved from optimized sensors, an optimization-based damage detection process is applied to validate the optimal sensor layout. Three case studies (i.e., a cantilever beam, a laminated composite structure, and a spatial frame) are presented to demonstrate the effectiveness and applicability of the developed framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  • An H, Chen S, Huang H (2018) Multi-objective optimization of a composite stiffened panel for hybrid design of stiffener layout and laminate stacking sequence. Struct Multidisc Optim 57(4):1411–1426

    Article  MathSciNet  Google Scholar 

  • An H, Youn BD, Kim HS (2021) Reliability-based design optimization of laminated composite structures under delamination and material property uncertainties. Int J Mech Sci 205:106561

    Article  Google Scholar 

  • An H, Youn BD, Kim HS (2022) A robust design framework to determine the optimal number and placement of sensors under uncertainty for vibration-based damage detection of composite structures. Compos Struct 279:114863

    Article  Google Scholar 

  • Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ (2021) A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mech Syst Signal Proc 147:107077

    Article  Google Scholar 

  • Balaban E, Bansal P, Stoelting P, Saxena A, Goebel KF, Curran S. A diagnostic approach for electro-mechanical actuators in aerospace systems. In: 2009 IEEE Aerospace conference, BigSky, MT; 2009, pp. 1–13. doi: https://doi.org/10.1109/AERO.2009.4839661

  • Barman SK, Mishra M, Maiti DK, Maity D (2021) Vibration-based damage detection of structures employing Bayesian data fusion coupled with TLBO optimization algorithm. Struct Multidisc Optim. 64: 2243–2266. https://doi.org/10.1007/s00158-021-02980-6

  • Bigoni C, Zhang Z, Hesthaven JS (2020) Systematic sensor placement for structural anomaly detection in the absence of damaged states. Comput Methods Appl Mech Engrg 371:113315

    Article  MathSciNet  MATH  Google Scholar 

  • Castro-Triguero R, Murugan S, Gallego R, Friswell MI (2013) Robustness of optimal sensor placement under parametric uncertainty. Mech Syst Signal Proc 41:268–287

    Article  Google Scholar 

  • Chisari C, Macorini L, Amadio C, Izzuddin BA (2017) Optimal sensor placement for structural parameter identification. Struct Multidisc Optim 55(2):647–662

    Article  MathSciNet  Google Scholar 

  • Danczyk A, Di X, Liu HX (2016) A probabilistic optimization model for allocating freeway sensors. Transp Res Pt C-Emerg Technol 67:378–398

    Article  Google Scholar 

  • Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Sys 9(2):115–148

    MathSciNet  MATH  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Dinh-Cong D, Dang-Trung H, Nguyen-Thoi T (2018) An efficient approach for optimal sensor placement and damage identification in laminated composite structures. Adv Eng Softw 119:48–59

    Article  Google Scholar 

  • Friswell MI, Castro-Triguero R (2015) Clustering of sensor locations using the effective independence method. AIAA J 53(5):1388–1390

    Article  Google Scholar 

  • Gomes GF, Cunha SS da da Silva Lopes Alexandrino P, de Sousa BS, Ancelotti AC (2018) Sensor placement optimization applied to laminated composite plates under vibration. Struct Multidisc Optim 58:2099–2118

    Article  MathSciNet  Google Scholar 

  • Jäger G, Zug S, Casimiro A (2018) Generic sensor failure modeling for cooperative systems. Sensors 18(3):925

    Article  Google Scholar 

  • Jayalakshmi V, Rao RM A (2017) Simultaneous identification of damage and input dynamic force on the structure for structural health monitoring. Struct Multidisc Optim 55(6):2211–2238

    Article  MathSciNet  Google Scholar 

  • Jesus G, Casimiro A, Oliveira A (2017) A survey on data quality for dependable monitoring in wireless sensor networks. Sensors 17(9):2010

    Article  Google Scholar 

  • Jin R, Chen W, Simpson TW (2001) Comparative studies of metamodelling techniques under multiple modelling criteria. Struct Multidisc Optim 23(1):1–13

    Article  Google Scholar 

  • Kammer DC (1991) Sensor placement for on-orbit modal identification and correlation of large space structures. J Guid Control Dyn 14(2):251–259

    Article  Google Scholar 

  • Kim T, Youn BD, Oh H (2018) Development of a stochastic effective independence (SEFI) method for optimal sensor placement under uncertainty. Mech Syst Signal Proc 111:615–627

    Article  Google Scholar 

  • Kullaa J (2013) Detection, identification, and quantification of sensor fault in a sensor network. Mech Syst Signal Proc 40(1):208–221

    Article  Google Scholar 

  • Li X, Ouyang Y (2011) Reliable sensor deployment for network traffic surveillance. Transp Res Pt B-Methodol 45(1):218–231

    Article  Google Scholar 

  • Li DS, Li HN, Fritzen CP (2007) The connection between effective independence and modal kinetic energy methods for sensor placement. J Sound Vibr 305(4–5):945–955

    Article  Google Scholar 

  • Lian J, He L, Ma B, Li H, Peng W (2013) Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm. Smart Mater Struct 22(9):095015

    Article  Google Scholar 

  • Liu W, Gao W-C, Sun Y, Xu M-J (2008) Optimal sensor placement for spatial lattice structure based on genetic algorithms. J Sound Vibr 317(1–2):175–189

    Article  Google Scholar 

  • Lu W, Wen R, Teng J, Li X, Li C (2016) Data correlation analysis for optimal sensor placement using a bond energy algorithm. Measurement 91:509–518

    Article  Google Scholar 

  • MacKay DJC (1998) Introduction to Gaussian processes. NATO ASI Ser F Comput Syst Sci 168:133–166

    MATH  Google Scholar 

  • Ostachowicz W, Soman R, Malinowski P (2019) Optimization of sensor placement for structural health monitoring: a review. Struct Health Monit 18(3):963–988

    Article  Google Scholar 

  • Rao ARM, Anandakumar G (2007) Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique. Smart Mater Struct 16:2658–2672

    Article  Google Scholar 

  • Ritter J (1990) An efficient bounding sphere. Graph Gems 1:301–303

    Google Scholar 

  • Salari M, Kattan L, Lam WHK, Lo HP, Esfeh MA (2019) Optimization of traffic sensor location for complete link flow observability in traffic network considering sensor failure. Transp Res Pt B-Methodol 121:216–251

    Article  Google Scholar 

  • Staszewski WJ, Worden K, Wardle R, Tomlinson GR (2000) Fail-safe sensor distributions for impact detection in composite materials. Smart Mater Struct 9(3):298–303

    Article  Google Scholar 

  • Sun H, Büyüköztürk O (2015) Optimal sensor placement in structural health monitoring using discrete optimization. Smart Mater Struct 24:125034

    Article  Google Scholar 

  • Tan Y, Zhang L (2020) Computational methodologies for optimal sensor placement in structural health monitoring: A review. Struct Health Monit 19(4):1287–1308

    Article  Google Scholar 

  • Welzl E (1991) Smallest enclosing disks (balls and ellipsoids). In: Maurer H (eds) New results and new trends in computer science .Springer Berlin, Heidelberg, 359–370

    Chapter  Google Scholar 

  • Williams CKI, Rasmussen CE (2006) Gaussian processes for machine learning. MIT press Cambridge, MA.

    MATH  Google Scholar 

  • Yang C (2018) Sensor placement for structural health monitoring using hybrid optimization algorithm based on sensor distribution index and FE grids. Struct Control Health Monit 25(6):e2160

    Article  Google Scholar 

  • Yang C (2021) An adaptive sensor placement algorithm for structural health monitoring based on multi-objective iterative optimization using weight factor updating. Mech Syst Signal Proc 151:107363

    Article  Google Scholar 

  • Yang C, Zheng W, Zhang X (2019a) Optimal sensor placement for spatial lattice structure based on three-dimensional redundancy elimination model. Appl Math Model 66:576–591

    Article  Google Scholar 

  • Yang C, Liang K, Zhang X, Geng X (2019b) Sensor placement algorithm for structural health monitoring with redundancy elimination model based on sub-clustering strategy. Mech Syst Signal Proc 124:369–387

    Article  Google Scholar 

  • Yang C, Liang K, Zhang X (2020) Strategy for sensor number determination and placement optimization with incomplete information based on interval possibility model and clustering avoidance distribution index. Comput Methods Appl Mech Engrg 366:113042

    Article  MathSciNet  MATH  Google Scholar 

  • Yi T-H, Li HN (2012) Methodology developments in sensor placement for health monitoring of civil infrastructures. Int J Distrib Sens Netw 8:612726

    Article  Google Scholar 

  • Zhang Z, He M, Liu A (2018) Vibration-based assessment of delaminations in FRP composite plates. Compos Part B-Eng 144:254–266

    Article  Google Scholar 

  • Zhao J, DeWolf JT (1999) Sensitivity study for vibrational parameters used in damage detection. J Struct Eng 125(4):410–416

    Article  Google Scholar 

  • Zhu N, Ma S, Zheng L (2017) Travel time estimation oriented freeway sensor placement problem considering sensor failure. J Intell Transport Syst 21(1):26–40

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Brain Korea 21 FOUR Project in 2020.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea

    Haichao An & Byeng D. Youn

  2. Institute of Advanced Machines and Design, Seoul National University, Seoul, 08826, Republic of Korea

    Byeng D. Youn

  3. OnePredict Inc., Seoul, 06160, Republic of Korea

    Byeng D. Youn

  4. Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, Seoul, 04620, Republic of Korea

    Heung Soo Kim

Authors
  1. Haichao An
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Byeng D. Youn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heung Soo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H. An wrote the manuscript using the computational results. BD. Youn and HS. Kim (heungsoo@dgu.edu) supervised the project, and they are co-corresponding authors of this paper.

Corresponding author

Correspondence to Byeng D. Youn.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Replication of results

Data and materials for replication of the case studies will be available from the corresponding author upon request.

Additional information

Responsible Editor: Chao Hu

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

An, H., Youn, B.D. & Kim, H.S. Optimal Sensor Placement Considering Both Sensor Faults Under Uncertainty and Sensor Clustering for Vibration-Based Damage Detection. Struct Multidisc Optim 65, 102 (2022). https://doi.org/10.1007/s00158-021-03159-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00158-021-03159-9

Keywords

Search

Navigation