Time-Dependent Reliability Analysis Using a Modified Composite Limit State Approach

Majcher, Monica; Mourelatos, Zissimos P.; Tsianika, Vasiliki

doi:10.4271/2017-01-0206

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…The first step is to determine whether the signs of samples in the sample pool D are all correct based on Eq. (23). If the stopping criterion of U-learning function in Eq.…”

Section: A New Updating Strategy For Pck Model Of Instantaneous Responsementioning

confidence: 99%

“…the prediction signs of samples in D are all credible based on the stopping criterion of U-learning function expressed in Eq. (23). If Eq.…”

Section: Implementation Proceduresmentioning

confidence: 99%

“…Except for the aforementioned two categories of classic methods, some different approaches for time-variant reliability analysis are also reported. Composite limit state methods (CLS) [23] discretize the time interval of interest to obtain the instantaneous limit state function as the components of a series system, and then the time-variant reliability is assessed by time-invariant series system reliability methods. Li et al [19] proposed an improved composite limit state method, Kriging models of all time nodes and one CLS are constructed to estimate the time-variant reliability.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An adaptive PC-Kriging method for time-variant structural reliability analysis

Nan¹,

Li²,

Song³

2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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The practical application of time-variant reliability analysis is limited by its computationally expensive models which describe the structural system behavior. This paper presents a new adaptive PC-Kriging (APCK) approach to accurately and efficiently assess the time-variant reliabilities. Time interval is firstly discretized with a series of time instants and then the stochastic process is reconstructed by standard normal random variables and deterministic function of time. PC-Kriging (PCK) models are built at each time instant to predict the instantaneous responses of performance function. To improve the accuracy and efficiency, a new update strategy based on the integration of U- and H- learning functions is developed to refine the PCK models of instantaneous responses. One or two best samples are identified by the proposed learning criterion for updating the PCK models. Finally, Monte Carlo simulation (MCS) is used to estimate the time-variant reliability based on the updated PCK models. Four examples are used to validate the accuracy and efficiency of the proposed method.

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“…The first step is to determine whether the signs of samples in the sample pool D are all correct based on Eq. (23). If the stopping criterion of U-learning function in Eq.…”

Section: A New Updating Strategy For Pck Model Of Instantaneous Responsementioning

confidence: 99%

“…the prediction signs of samples in D are all credible based on the stopping criterion of U-learning function expressed in Eq. (23). If Eq.…”

Section: Implementation Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive PC-Kriging method for time-variant structural reliability analysis

Nan¹,

Li²,

Song³

2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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show abstract

“…Zhang employed a response surface with a very small number of RS modeling iterations and sampling functional to estimate time-dependent reliability for a practical problem (Zhang et al, 2017). Majcher proposed a novel composite limit-state method to calculate the time-dependent probability of failure for dynamic systems (Majcher et al, 2017). Hu studied a more accurate method that relaxes the assumption by using joint upcrossing rates and applied to the reliability analysis (Hu and Du, 2013).…”

Section: Introductionmentioning

confidence: 99%

Time-dependent reliability analysis of the motor hanger for EMU based on stochastic process

Zhi

Chen

2019

IJSI

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Purpose Most of the previous work on reliability analysis was based on the traditional reliability theory. The calculated results can only reflect the reliability of components at a specific time, which neglects the uncertainty of load and resistance over time. The purpose of this paper is to develop a time-dependent reliability analysis approach based on stochastic process to deal with the problem and apply it to the structural design of railway vehicle components. Design/methodology/approach First, the parametric model of motor hanger for electric multiple unit (EMU) is established by ANSYS parametric design language, and its structural stress is analyzed according to relevant standards. The Latin hypercube method is used to analyze the sensitivity of the structure, and the uncertainty parameters (sizes and loads) which have great influence on the structural strength are determined. The D-optimal experimental design is carried out to establish the polynomial response surface function, which characterizes the relationship between uncertainty parameters and structural stress. Second, the Poisson stochastic process is adopted to describe the number of loads acting, and the Monte Carlo method is used to obtain the load acting history according to its probability distribution characteristics. The load history is introduced into the response surface function and the uncertainty of other parameters is considered at the same time, and the stress history of the motor hanger is obtained. Finally, the degradation process of structural resistance is described by a Gamma stochastic process, and the time-dependent reliability of the motor hanger is calculated based on the reliability theory. Findings Time and the uncertainties of parameters have great impact on reliability. The results of reliability decrease with time fluctuation are more reasonable, stable and credible than traditional methods. Practical implications In this paper, the proposed method is applied to the structural design of the motor hanger for EMU, which has a good guiding significance for accurately evaluating whether if the design meets the reliability requirements. Originality/value The value of this paper is that the method takes both the randomness of load over time and the uncertainty of structural parameters in the design and manufactures process into consideration, and describes the monotonous degradation characteristics of structural resistance. At the same time, the time-dependent reliability of mechanical components is calculated by a response surface method. It not only improves the accuracy of reliability analysis, but also improves the analysis efficiency and solves the problem that the traditional reliability analysis method can only reflect the static reliability of components.

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Time-variant reliability assessment for multiple failure modes and temporal parameters

Wang

Meng

2018

Struct Multidisc Optim

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Time-Dependent Reliability Analysis Using a Modified Composite Limit State Approach

Cited by 9 publications

References 15 publications

An adaptive PC-Kriging method for time-variant structural reliability analysis

An adaptive PC-Kriging method for time-variant structural reliability analysis

Time-dependent reliability analysis of the motor hanger for EMU based on stochastic process

Time-variant reliability assessment for multiple failure modes and temporal parameters

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