A coupled subset simulation and active learning kriging reliability analysis method for rare failure events

Abstract It is generally understood that intractable computational intensity stemming from repeatedly calling performance function when evaluating the contribution of joint focal elements hinders the application of evidence theory in practical engineering. In order to promote the practicability of evidence theory for the reliability evaluation of engineering structures, an efficient reliability analysis method based on the active learning Kriging model is proposed in this study. To start with, a basic variable is selected according to basic probability assignment (BPA) of evidence variables to divide the evidence space into sub-evidence spaces. Intersection points between the performance function and the sub-evidence spaces are then determined by solving the univariate root-finding problem. Sample points are randomly identified to enhance the accuracy of the subsequently established surrogate model. Initial Kriging model with high approximation accuracy is subsequently established through these intersection points and additional sample points generated by Latin hypercube sampling. An active learning function is employed to sequentially refine the Kriging model with minimal sample points. As a result, belief (Bel) measure and plausibility (Pl) measure are derived efficiently via the surrogate model in the evidence-theory-based reliability analysis. The currently proposed analysis method is exemplified with three numerical examples to demonstrate the efficiency and is applied to reliability analysis of positioning accuracy for an industrial robot.

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The Study of Reliability Analysis Method Considering The Fault Characteristics of Active Phased Array Antenna

Journal of Applied Reliability ◽

10.33162/jar.2018.12.18.4.291 ◽

2018 ◽

Vol 18 (4) ◽

pp. 291-300

Author(s):

Seungsang Lee ◽

Ilhoon Cho ◽

Junghoon Lee ◽

Yeonkyeong Park

Keyword(s):

Reliability Analysis ◽

Phased Array ◽

Array Antenna ◽

Analysis Method ◽

Phased Array Antenna

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An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties

Algorithms ◽

10.3390/a14080229 ◽

2021 ◽

Vol 14 (8) ◽

pp. 229

Author(s):

Fangyi Li ◽

Yufei Yan ◽

Jianhua Rong ◽

Houyao Zhu

Keyword(s):

Reliability Analysis ◽

Limit State ◽

Random Variables ◽

Equivalent Model ◽

Independent Random Variables ◽

Problem Analysis ◽

Analysis Method ◽

Calculation Algorithm ◽

Reliability Problem ◽

Interval Variables

In practical engineering, due to the lack of information, it is impossible to accurately determine the distribution of all variables. Therefore, time-variant reliability problems with both random and interval variables may be encountered. However, this kind of problem usually involves a complex multilevel nested optimization problem, which leads to a substantial computational burden, and it is difficult to meet the requirements of complex engineering problem analysis. This study proposes a decoupling strategy to efficiently analyze the time-variant reliability based on the mixed uncertainty model. The interval variables are treated with independent random variables that are uniformly distributed in their respective intervals. Then the time-variant reliability-equivalent model, containing only random variables, is established, to avoid multi-layer nesting optimization. The stochastic process is first discretized to obtain several static limit state functions at different times. The time-variant reliability problem is changed into the conventional time-invariant system reliability problem. First order reliability analysis method (FORM) is used to analyze the reliability of each time. Thus, an efficient and robust convergence hybrid time-variant reliability calculation algorithm is proposed based on the equivalent model. Finally, numerical examples shows the effectiveness of the proposed method.

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Comparison of reliability evaluation methods between China and Canada standards for wood structures featuring duration of load

Journal of Wood Science ◽

10.1186/s10086-020-01920-0 ◽

2020 ◽

Vol 66 (1) ◽

Author(s):

Qiongyao Wu ◽

Shuang Niu ◽

Enchun Zhu

Keyword(s):

Reliability Analysis ◽

Analysis Method ◽

Safety Level ◽

Wood Structures ◽

Load Effect ◽

Key Factor ◽

Live Loads ◽

High Level ◽

Partial Factor ◽

Duration Of Load

Abstract Duration of load (DOL) is a key factor in design of wood structures, which makes the reliability analysis of wood structures more complicated. The importance of DOL is widely recognized, yet the methods and models through which it is incorporated into design codes vary substantially by country/region. Few investigations of the effect of different model assumptions of DOL and other random variables on the results of reliability analysis of wood structures can be found. In this paper, comparisons are made on the reliability analysis methods that underlie the China and the Canada standards for design of wood structures. Main characteristics of these two methods, especially the way how DOL is treated are investigated. Reliability analysis was carried out with the two methods employing the same set of material properties and load parameters. The resulted relationships between reliability index β and resistance partial factor γR* (the β–γR* curves) for four load combinations are compared to study the safety level indicated by the two methods. The comparison shows that the damage accumulation model (Foschi–Yao model) in the Canada analysis method is highly dependent on the type and duration of load, resulting in more conservative design than the China analysis method in loading cases dominated by dead load, but less conservative design in cases of high level of live loads. The characteristics of the load effect term of the performance function are also found to make considerable difference in reliability levels between the two methods. This study aims to provide references for researchers and standard developers in the field of wood structures.

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