Efficiency Of Reliability Analysis Research Articles

Parallel adaptive ensemble of metamodels combined with hypersphere sampling for rare failure events

In practical engineering, especially in aeronautical engineering, the failure probability is extremely rare due to the incorporation of safety factors in the mechanical design phase. Consequently, a significant challenge is to assess the reliability of mechanical products with implicit functions and rare failure events. To address this issue, this work presents a parallel adaptive ensemble of metamodels (EM) coupled with hypersphere sampling strategy to improve the accuracy and efficiency of reliability analysis. The proposed method consists of three main features. First, a new heuristic ensemble strategy is proposed to provide a powerful and robust metamodel. Second, a n-dimensional uniform sampling technique with better space-filling ability is taken to improve efficiency, which leads to a decrease in the extensive sample size required to capture rare failure events. Third, an effective parallel enrichment strategy is developed by the proposed pseudo-improved U learning function. When parallel computation is possible, the proposed method can select a batch of informative updated points simultaneously to update the EM. Three numerical examples and a planar ten-bar structure are presented to demonstrate the accuracy and efficiency of the proposed method. This method is also applied to the reliability assessment of the aircraft lock mechanism.

Efficient reliability analysis using prediction-oriented active sparse polynomial chaos expansion

In this paper, a prediction-oriented active sparse polynomial chaos expansion (PAS-PCE) is proposed for reliability analysis. Instead of leveraging on additional techniques to reduce the problem dimensionality and/or to obtain the local error estimates, which has been done in the majority of existing PCE-based methods, this study first makes use of the Bregman-iterative greedy coordinate descent in effectively solving the least absolute shrinkage and selection operator based regression for sparse PCE approximation with a small set of initial samples. Then, the local variance distribution of the performance function is predicted using the approximated PCE. By maximizing an optimality measure that balances the exploration of design space and exploitation of the PCE characteristics, a recently proposed learning function is subsequently adopted for selecting the optimal samples one by one from a candidate pool to cover the limit state surface regions proportionally to the predicted local variance. The performance of the proposed PAS-PCE is assessed on four numerical examples of varying complexity and input dimensionality through comparison with several state-of-the-art active learning methods based on a variety of surrogate models. Results show that the proposed method is superior to the benchmark algorithms in terms of both accuracy and efficiency for reliability analysis.

An active learning Kriging-based method combining the weight information entropy function and the adaptive candidate sample pool

The reliability analysis methods based on the Kriging model have been explored to update the design of experiments (DoE), but the number of calls to the performance function is high. In this paper, a new active learning method combining the weight information entropy function (WH) and the adaptive candidate sample pool is proposed to improve the reliability analysis efficiency. Based on the information entropy function (H), the learning function WH is constructed to consider Kriging variance and the joint probability density function (PDF). In this proposed method, the sample points can be assigned different weight by the learning function WH according to the important degrees of sample points. The point which not only nears the limit state function (LSF), but also has a high probability density function value and large Kriging variance is assigned more weight than others. To select the sample points with lower confidence level, the adaptive candidate sample pool is generated by Markov Chain Monte Carlo (MCMC) simulation. The Kriging model can be updated efficiently by the proposed method. Four numerical examples and an engineering example with implicit performance function are used to verify the efficiency and accuracy of the proposed method. The results show that the proposed method can significantly improve the computational efficiency of the reliability analysis without losing accuracy.

A new unequal-weighted sampling method for efficient reliability analysis

In this paper, a new method for efficient reliability analysis is proposed. The proposed method utilizes the Voronoi cells to partition the random-variate space into several sub-spaces and the kernel density estimation to approximate the failure probability in each sub-space. The optimal bandwidth for the kernel is also suggested. Then, the failure probability can be conveniently evaluated by a weighted summation over each sub-space (sampling point). Since the weight for each sub-space (sampling point) is not identical, this method is referred to as the unequal-weighted sampling method for reliability analysis. Numerical implementation procedure of the proposed method is also outlined. Several numerical examples are investigated to verify the proposed method, where the results are compared with those of Monte Carlo simulation and subset simulation methods. It is demonstrated that the proposed method can achieve the tradeoff of accuracy and efficiency for reliability analysis. Problems to be further studied are also pointed out.

An efficient reliability method combining adaptive Support Vector Machine and Monte Carlo Simulation

To enhance computational efficiency in reliability analysis, metamodeling has been widely adopted for reliability assessment. This work develops an efficient reliability method which takes advantage of the Adaptive Support Vector Machine (ASVM) and the Monte Carlo Simulation (MCS). A pool-based ASVM is employed for metamodel construction with the minimum number of training samples, for which a learning function is proposed to sequentially select informative training samples. Then MCS is employed to compute the failure probability based on the SVM classifier obtained. The proposed method is applied to four representative examples, which shows great effectiveness and efficiency of ASVM-MCS, leading to accurate estimation of failure probability with rather low computational cost. ASVM-MCS is a powerful and promising approach for reliability computation, especially for nonlinear and high-dimensional problems.

Reliability-based low-cycle fatigue damage analysis for turbine blade with thermo-structural interaction

To improve the computational accuracy and efficiency of complex mechanical component like engine turbine structure, distributed collaborative response surface method is applied to the reliability analysis of aeroengine turbine blade low-cycle fatigue damage. The improved Manson–Coffin formulas with different confidence levels are established based on the linear variance regression analysis in the application of the fatigue data of nickel-based superalloy GH4133. The distributed response surfaces of strain range Δεt and mean stress σm are established by considering the randomness of the design sizes, working loads and material parameters. And then Δεt and σm are regarded as the basic input variables of fatigue life Nf to complete turbine blade low-cycle fatigue damage reliability analysis by the Miner cumulative damage theory. The probabilistic sensitivity analyses demonstrate that the fatigue performance parameters hold important influence on the low-cycle fatigue life of turbine blade. Through the comparison of methods, it is revealed that distributed collaborative response surface method is superior to response surface method in computational precision and efficiency, especially for low confidence level. The efforts give the conclusion that distributed collaborative response surface method is a promising approach in ameliorating the computational precision and efficiency of reliability analysis, which enriches the reliability theory and method of complex mechanical structure with multi-component and multi-failure mode.

Cross-Layer Modeling and Simulation of Circuit Reliability

Integrated circuit design in the late CMOS era is challenged by the ever-increasing variability and reliability issues. The situation is further compounded by real-time uncertainties in workload and ambient conditions, which dynamically influence the degradation rate. To improve design predictability and guarantee system lifetime, accurate modeling, and simulation tools for reliability are essential to both digital and analog circuits. This paper presents cross-layer solutions for emerging reliability threats, including: 1) device-level modeling of reliability mechanisms, such as transistor aging and its statistical behavior; 2) circuit-level long-term aging models that capture unique operation patterns in digital and analog design, and directly predict the degradation; and 3) simulation methods for very-large-scale designs. Built on the long-term model, the new methods significantly enhance the accuracy and efficiency of reliability analysis. As validated by silicon data, these solutions close the gap between the underlying reliability physics and circuit/system design for resilience.

The Variation Coefficient Method for Reliability Sensitivity Analysis of Cylindrical Helical Spring

For mechanical reliability design with more random variable parameters, if the parameters are queued with their impact on the sensitivity of mechanical reliability, which will be as random variables with relatively high impact, while low-impact will be determined variables, thus would simplify reliability analysis due to the random variable number reduced. With cylindrical helical spring as an example, applying the variation coefficient method, the reliability sensitivity calculation of shear stress and deformation is discussed, and the impact on the reliability of cylindrical helical spring by changing the random variables is also studied. Using the variation coefficient method we can obtain the reliability sensitivity of cylindrical helical spring quickly and accurately, which provides a theoretical basis for the reliability design of cylindrical helical spring, and has greatly improved the reliability analysis efficiency of cylindrical helical spring.