Sensitivity analysis with MC simulation for the failure rate evaluation and reliability assessment

Catelani, Marcantonio; Ciani, Lorenzo; Venzi, Matteo

doi:10.1016/j.measurement.2015.07.003

Cited by 27 publications

(8 citation statements)

References 23 publications

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“…The proposed method is verified by MCSM. Compared with some reliability methods, for example, FORM and SORM (Melchers et al., 2003; Saassouh and Lounis, 2012), DIM (Chai et al., 2016), SEAM (Song et al., 2015), FEM and MCSM (Li et al., 2010; Catelani et al., 2015), the proposed method does not depend on the analytical expression of limit-state function, and avoids the difficulties of defining failure domains and computing the multi-dimensional joint probability density function of random responses. It well adapts to multi-random parameters problems.…”

Section: Discussionmentioning

confidence: 99%

“…The multi-dimensional joint probability density function of random parameters involved are not modeled very well. The MCSM can be used to calculate the responses of structures with many uncertain parameters involved while it contributes most to calculation, even for simple structures (Padmanabhan et al., 2006; Catelani et al., 2015). So, it is not a good idea to use it for complex structures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic reliability prediction of vehicular suspension structure with damping random uncertainties

Wang

Song

Chen

et al. 2018

Journal of Vibration and Control

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This research is conducted such that a two degrees of freedom nonlinear stochastic vibration model of vehicular suspension structure with random bilinear damping forces and cubic nonlinear restoring forces is established. Based on extreme value theory (EVT) and Monte Carlo integration method (MCIM), a novel method of predicting the failure probabilities of the maximum amplitude responses of suspension structure is proposed. The extreme value distribution functions of amplitude responses, which are analytical expressed by underlying amplitude distribution functions, are acquired by using EVT. In order to obtain the analytical expressions of the underlying distribution functions, histograms are used to estimate their distribution forms. Under the log-normal distribution assumption, the means and standard deviations of the underlying distributions are calculated by using MCIM, and then by setting up amplitude failure levels, the extreme distribution functions and failure probabilities of amplitude responses are obtained. The Monte Carlo simulation method-based numerical analyses give enormous supports to use of the proposed prediction means.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic reliability prediction of vehicular suspension structure with damping random uncertainties

Wang

Song

Chen

et al. 2018

Journal of Vibration and Control

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“…Before that, a geophysical survey of the tumulus has been carried out by the DST research group to verify the state of conservation of buried structures and to identify the ancient access of the tomb. Thanks to the collaboration with the DINFO research group, an in-depth statistical analysis of the acquired data was carried out by means of a Monte Carlo (MC) simulation [35,36]. MC method was applied to evaluate the influence of the GPS-error on the definition of the geometric factor and consequently on the indirect measurement of the apparent resistivity.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of the GPS Errors Occurred While Collecting Electrode Coordinates on the Electrical Resistivity of Tumuli

Pazzi

Ceccatelli

Ciani

et al. 2020

Sensors

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In archaeological applications the accurate reconstruction of buried structures is mandatory. Electrical resistivity tomography is widely used for this purpose. Nevertheless, resistivity errors could be generated by wrong placement of electrodes. Papers in the literature do not discuss the influence of errors connected with the electrode position location (GPS-error). In this paper the first results of a Monte Carlo simulation analysis of data acquired on a tumulus are presented. The main research questions were: (i) if it is correct to ignore the GPS-error collect, and (ii) if a minimum threshold, that significantly affect the inversion, exists. Results, obtained considering planimetric GPS-errors of about one third of the fixed electrode distances, show that the GPS-errors affect resistivity, but the generated errors/anomalies: (a) are lower than that obtained without considering the topography, and (b) are significant from a numerical point of view, but do not affect the interpretation, being compatible with the soil resistivity ranges.

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“…Physical knowledge, expert information and data on the system behavior are used to build the model and estimate its parameters (Aven & Zio, 2011;Aven et al, 2014). The uncertainties in the model and parameters can be propagated by Monte Carlo (MC) simulation (Zio & Pedroni, 2009;Zio & Pedroni, 2012;Zhang et al, 2010;Catelani et al, 2015), Bayesian posterior analysis (Zhang & Mahadevan, 2001) and Fuzzy methodology (Dubais, 2010;Baraldi et al, 2015a;Garg, 2013;Garg, 2014). Most commonly, MC simulation is used, consisting in repeatedly sampling random values of the inputs from probability distributions (Zio, 2013).…”

Section: Introductionmentioning

confidence: 99%

Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment

Wang

Maio

Zio

2017

Reliability Engineering & System Safety

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Multi-State Physics Modeling (MSPM) provides a physics-based semiMarkov modeling framework for a more detailed reliability assessment. In this work, a three-loop Monte Carlo (MC) simulation scheme is proposed to operationalize the MSPM approach, quantifying and controlling the uncertainty affecting the system reliability model. The proposed MC simulation scheme involves three steps: i) the identification of the system components that deserve MSPM, ii) the quantification of the uncertainties in the MSPM component models and their propagation onto the system-level model, and iii) the selection of the most suitable modeling alternative that balances the computational demand for the system model solution and the robustness of the system reliability estimates.A Reactor Protection System (RPS) of a Nuclear Power Plant (NPP) is considered as case study for numerical evaluation.

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Sensitivity analysis with MC simulation for the failure rate evaluation and reliability assessment

Cited by 27 publications

References 23 publications

Dynamic reliability prediction of vehicular suspension structure with damping random uncertainties

Dynamic reliability prediction of vehicular suspension structure with damping random uncertainties

Analysis of the Influence of the GPS Errors Occurred While Collecting Electrode Coordinates on the Electrical Resistivity of Tumuli

Three-loop Monte Carlo simulation approach to Multi-State Physics Modeling for system reliability assessment

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