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Interval Pareto front-based multi-objective robust optimization for sensor placement in structural modal identification

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  • Yang, Chen
  • Xia, Yuanqing

Abstract

Considering the multi-performance development of complex systems and the requirement of structural modal identification with typical uncertainties, the nominal single-objective optimization method is not suitable for sensor placement. Therefore, by combining conventional optimal sensor placement with non-probabilistic theory, this study proposes an uncertainty-oriented multi-objective robust optimization method for optimal sensor placement. The Fisher information matrix and ill-posedness comprise one eigenvalue-based optimization objective, and the mean and minimum off-diagonal values in the modal assurance criterion comprise another. Considering the high-cost limitation of the statistical method for handling uncertainties, uncertainty propagations are realized by a dimension-wise analysis with better accuracy and efficiency, thus avoiding the overestimation incurred by the classical Taylor expansion method. The multi-objective robust optimization is established by uncertain eigenvalue- and eigenvector-based indices with interval numbers and solved using the multi-objective optimization algorithm. Considering the solution sets located at the Pareto front, an interval possibility was developed using interval Pareto fronts to determine the optimal number of sensors. A numerical example demonstrated the validity of the proposed method with an optimal number of sensors and corresponding configurations.

Suggested Citation

  • Yang, Chen & Xia, Yuanqing, 2024. "Interval Pareto front-based multi-objective robust optimization for sensor placement in structural modal identification," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006178
    DOI: 10.1016/j.ress.2023.109703
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    1. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Wang, Lei & Liu, Yaru & Li, Min, 2022. "Time-dependent reliability-based optimization for structural-topological configuration design under convex-bounded uncertain modeling," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Malings, C. & Pozzi, M., 2019. "Submodularity issues in value-of-information-based sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 93-103.
    4. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Casualty analysis methodology and taxonomy for FPSO accident analysis," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    5. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    6. Yang, Chen & Lu, Wanze & Xia, Yuanqing, 2023. "Reliability-constrained optimal attitude-vibration control for rigid-flexible coupling satellite using interval dimension-wise analysis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Coraça, Eduardo M. & Ferreira, Janito V. & Nóbrega, Eurípedes G.O., 2023. "An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. Liu, Jiaxiang & Wang, Lei, 2023. "Two-stage vibration-suppression framework for optimal robust placements design and reliable PID gains design via set-crossing theory and artificial neural network," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Chadha, Mayank & Ramancha, Mukesh K. & Vega, Manuel A. & Conte, Joel P. & Todd, Michael D., 2023. "The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    11. Farahmand, Hamed & Liu, Xueming & Dong, Shangjia & Mostafavi, Ali & Gao, Jianxi, 2022. "A Network Observability Framework for Sensor Placement in Flood Control Networks to Improve Flood Situational Awareness and Risk Management," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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    2. Luo, Zhenxian & Zhang, Yingke & Fang, Hongyu & Wang, Chuanjian & Wang, Lei, 2025. "Prediction for the structural response with multi-extreme points based on the third-order Taylor series expansion subinterval method," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    3. Ma, Yuan-Zhuo & Li, Chen-Xu & Li, Chuang & Yuan, Wei-Hai & Ding, A-Nai & Zhao, Zhen-Zhou & Li, Hong-Shuang, 2026. "A double-loop adaptive Kriging-based robust design optimization method with aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
    4. Li, Yanbin & Hu, Weikun & Zhang, Feng & Li, Yun, 2025. "Multi-objective collaborative operation optimization of park-level integrated energy system clusters considering green power forecasting and trading," Energy, Elsevier, vol. 319(C).
    5. Chao Han & Yun Zhu & Xing Zhou & Xuejie Wang, 2025. "Stochastic Optimization Scheduling Method for Mine Electricity–Heat Energy Systems Considering Power-to-Gas and Conditional Value-at-Risk," Energies, MDPI, vol. 18(15), pages 1-22, August.

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