A single-loop strategy for time-variant system reliability analysis under multiple failure modes

Qian, Huaming; Huang, Tudi; Huang, Hong‐Zhong

doi:10.1016/j.ymssp.2020.107159

Cited by 44 publications

(24 citation statements)

References 40 publications

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“…The proof also covers existing applications of multivariate normal models. 77 The above result allows for at least two interesting uses. First, our result allows for distributed calculus in kriging.…”

Section: Predictionmentioning

confidence: 92%

Separable spatio‐temporal kriging for fast virtual sensing

Miniato

Bellio

Grassetti

et al. 2022

Appl Stoch Models Bus & Ind

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Environmental monitoring is a task that requires to surrogate system-wide information with limited sensor readings. Under the proximity principle, an environmental monitoring system can be based on the virtual sensing logic and then rely on distance-based prediction methods, foremostly spatio-temporal kriging. The last one is cumbersome with large datasets, but we show that a suitable separability assumption reduces its computational cost to an extent broader than considered typically. Only spatial interpolation needs to be performed in a centralized way, while forecasting can be delegated to each sensor. This simplification is related to the fact that two separate models are involved, one in time and one in the space domain. Any of the two models can be replaced without re-estimating the other under a composite likelihood (CL) approach. Moreover, the use of convenient spatial and temporal models eases up computation, not only in the CL approach, but also in maximum likelihood estimation. We show that this perspective on kriging allows to perform virtual sensing even in the case of tall datasets.

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Section: Predictionmentioning

confidence: 92%

Separable spatio‐temporal kriging for fast virtual sensing

Miniato

Bellio

Grassetti

et al. 2022

Appl Stoch Models Bus & Ind

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“…Qian et al. (2021a, b) explored a new multiobjective dynamic reliability method based on the multiple-response Gaussian process and subset simulation, which is used to solve a small failure probability problem. Wu et al.…”

Section: Dynamic Reliability Analysis Of Multiobjective Structurementioning

confidence: 99%

“…Qian et al. (2021a, b) developed a single-loop strategy based on the multiple response Gaussian process and the Kriging model, which improved the efficiency of multifailures reliability analysis. In addition, some scholars have explored a new idea of using the maximum entropy method to solve multifailure modes.…”

Section: Dynamic Reliability Analysis Of Multiobjective Structurementioning

confidence: 99%

Structural dynamic reliability analysis: review and prospects

Teng

Feng

Chen

et al. 2022

IJSI

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PurposeThe purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.Design/methodology/approachIn this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.FindingsThe numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.Originality/valueThe paper provides a useful reference for further theoretical research and engineering application.

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“…Later, Hu and Mahadevan [19] proposed a single-loop GP approach where training points of random variables over time are generated at once (instead of tracking time and maximum responses separately). Qian et al [20] also proposed a single-loop strategy for time-variant system reliability analysis by combining multiple response Gaussian process models. Many surrogate methods are only applicable to cases where no input random process are involved.…”

Section: Introductionmentioning

confidence: 99%

“…and stochastic load processes, the reliability problem comprises a time-variant structure (system), which is subjected to time-variant loads. In this context, most of the current methods for reliability are based on approximate analytical methods, i.e., FORM [23] [24], or resort to surrogate models, i.e., through building extreme value surrogate models, as in [29,18,19,20,21,22], which have their own potential shortcomings. In the context of simulation-based methods, MCS and subset simulation can be used to solve reliability problems involving time-variant structures subject to stochastic load.…”

Section: Introductionmentioning

confidence: 99%