Structural reliability analysis under evidence theory using the active learning kriging model

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush–Kuhn–Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.     

Adaptive surrogate model coupled with stochastic configuration network strategies for time-dependent reliability assessment

Time-dependent reliability assessment is crucial in enhancing product development economics and product performance sustainability throughout the lifecycle. It is still a challenge to accurately and efficiently evaluate the time-dependent reliability of engineering systems. This paper proposes a novel adaptive surrogate model method combining stochastic configuration network (SCN) and Kriging strategies to evaluate time-dependent reliability. SCN has accurate approximation ability and learning efficiency for strongly nonlinear systems that can overcome the conventional time-dependent reliability calculation, which is time-consuming and characterized by low accuracy. The proposed method first applies SCN to establish the response model of the performance function with respect to time and obtain the extreme value of the performance function. Then, Kriging is used to establish the extreme value model of the performance function with respect to the random variables based on the extreme value of performance function. The adaptive process considering the characteristics of random variables samples is adopted to update the extreme value model until the model meets the confidence target. Lastly, Monte Carlo simulation is employed for time-dependent reliability assessment based on the established extreme value model. Three example studies are used to demonstrate the effectiveness of the proposed approach for time-dependent reliability assessment.     

A Bayesian model averaging method for software reliability modeling and assessment

The software reliability modeling is of great significance in improving software quality and managing the software development process. However, the existing methods are not able to accurately model software reliability improvement behavior because existing single model methods rely on restrictive assumptions and combination models cannot well deal with model uncertainties. In this article, we propose a Bayesian model averaging (BMA) method to model software reliability. First, the existing reliability modeling methods are selected as the candidate models, and the Bayesian theory is used to obtain the posterior probabilities of each reliability model. Then, the posterior probabilities are used as weights to average the candidate models. Both Markov Chain Monte Carlo (MCMC) algorithm and the Expectation–Maximization (EM) algorithm are used to evaluate a candidate model's posterior probability and for comparison purpose. The results show that the BMA method has superior performance in software reliability modeling, and the MCMC algorithm performs better than EM algorithm when they are used to estimate the parameters of BMA method.     

A combined fuzzy DEMATEL and cloud model approach for risk assessment in process industries to improve system reliability

Decision-making trial and evaluation laboratory (DEMATEL) analysis is an effective and comprehensive method for identifying accident factors and converting the relationships among them into a visual structural model. Traditionally, the mean value method is adopted to summarize the initial direct-relation matrix, but it ignores the errors caused by differences in expert knowledge. In addition, a single qualitative risk assessment may not be sufficiently comprehensive and persuasive. The qualitative risk assessment results may not play a complete role in helping industrial plants carry out safety management. Therefore, this study proposes a quantitative risk assessment model based on the cloud model (CM) called the fuzzy DEMATEL-CM. An assessment index model is established by identifying the hazards associated with a converter steelmaking system. Subsequently, fuzzy DEMATEL analysis is applied to determine the relationships among the assessment indices and calculate their weights. Then, the CM is utilized to calculate the risk levels of the assessment indices and determine the comprehensive risk level. Finally, a case study is introduced to verify the practicability and validity of this model, and it is observed that the model has a certain superiority in solving uncertain problems. The quantitative risk assessment results are helpful for preventing accidents to improve the reliability of converter steelmaking plants.     

Efficient conditional probability theorem and importance sampling-based methods for global reliability sensitivity analysis

Global reliability sensitivity (GRS) analysis can measure the effect of random inputs on failure probability (FP). To efficiently solve GRS, two conditional probability theorem (CPT)-based methods are proposed by combining adaptive Kriging (AK) with importance sampling (IS) (CPT-AK-IS) and combining AK with Meta-IS (CPT-AK-Meta-IS) respectively. Firstly, differentiation approximation and CPT are used to convert the estimation of conditional probability density function (PDF), which is required by the existing Bayes theorem-based methods, into that of a series of probabilities. Secondly, GRS can be directly estimated by the failure samples of IS, while the existing Bayes theorem methods based on IS need to transform the failure samples of IS into those of original PDF. Both the first and second strategies can reduce the computational complexity of solving GRS. Thirdly, by selecting a suitable differentiation interval with a proposed adaptive strategy, the estimation of a series of probabilities can be accurately completed as a byproduct of one IS based simulation for solving FP without additional computational cost. Finally, by introducing AK into IS and Meta-IS, it can reduce the number of evaluating performance function and the size of candidate sample pool simultaneously. These novelties are sufficiently verified by the presented examples.     

A generic method for estimating system reliability using Bayesian networks

This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples.     

A posteriori error estimates and adaptive procedures for the meshless Galerkin boundary node method for 3D potential problems

The Galerkin boundary node method (GBNM) is a boundary only meshless method that combines variational formulations of boundary integral equations with the moving least-squares approximations. This paper presents the mathematical derivation of a posteriori error estimates and adaptive refinement procedures for the GBNM for 3D potential problems. Two types of error estimators are developed in detail. One is a perturbation error estimator that is formulated based on the difference between numerical solutions obtained using two successive nodal arrangements. The other is a projection error estimator that is formulated based on the difference between the GBNM solution itself and its L²-orthogonal projection. The reliability and efficiency of both types of error estimators is established. That is, these error estimators are proven to have an upper and a lower bound by the constant multiples of the exact error in the energy norm. A localization technique is introduced to accommodate the non-local property of integral operators for the needed local and computable a posteriori error indicators. Convergence analysis results of corresponding adaptive meshless procedures are also given. Numerical examples with high singularities illustrate the theoretical results and show that the proposed adaptive procedures are simple, effective and efficient.     

基于模型修正的空间变异边坡可靠度分析方法

针对三阶矩拟正态变换理论公式系数形式复杂及现有相关系数的转换公式适用范围未明确的问题,通过对公式系数进行简化和对相关系数的讨论,提出了独立随机变量和相关随机变量的简化三阶矩拟正态变换模型,并给出了相关系数转换公式的简明适用范围。通过将提出的简化三阶矩拟正态变换模型与一阶可靠度分析方法(FORM)结合,发展了随机变量分布未知条件下的可靠度分析方法,并采用数值算例验证了该方法的准确性和适用性。研究结果表明,所提出的简化三阶矩拟正态变换模型具有较高的准确性和适用性,能够与FORM分析方法结合,实现随机变量分布未知条件下的结构可靠度分析。     

Error-based stopping criterion for the combined adaptive Kriging and importance sampling method for reliability analysis

Metamodel-based method is a wise reliability analysis technique because it uses the metamodel to substitute the actual limit state function under the predefined accuracy. Adaptive Kriging (AK) is a famous metamodel in reliability analysis for its flexibility and efficiency. AK combined with the importance sampling (IS) method abbreviate as AK–IS can extremely reduce the size of candidate sampling pool in the updating process of Kriging model, which makes the AK-based reliability method more suitable for estimating the small failure probability. In this paper, an error-based stopping criterion of updating the Kriging model in the AK–IS method is constructed and two considerable maximum relative error estimation methods between the failure probability estimated by the current Kriging model and the limit state function are derived. By controlling the maximum relative error, the accuracy of the estimate can be adjusted flexibly. Results in three case studies show that the error-based stopping criterion based AK–IS method can achieve the predefined accuracy level and simultaneously enhance the efficiency of updating the Kriging model.     

A reliability-based robust optimization design for the drum brake using adaptive Kriging surrogate model

To decrease random parameters’ influence on the drum brake reliability, the reliability-based robust optimization design (RBROD) of the electric vehicle brake is proposed. Based on the assumption that the maximum temperature of the brake cannot exceed the allowable temperature, a performance function model of thermal–mechanical coupling reliability of drum brakes is established by the adaptive Kriging method, and the analysis of reliability sensitivity and RBROD are conducted. The accuracy of the proposed model is verified by temperature measurement experiment under emergency braking condition. The robust optimization design improves the drum brake reliability to 0.99998 and reduce the influence of the design parameters on the reliability, with the absolute values of the reliability sensitivity and the weight of the drum brake are significantly smaller. Therefore, the objectives of reliability design, robustness design, and optimization design are simultaneously achieved by the proposed methods. Besides, the relative error of the proposed method is 0.373%, the number of function evaluations is 39, and the comparison with four meta-model methods show that the proposed method holds high-accuracy and high-efficiency. This study provides a high-precision theoretical explanation for the robust optimization design of drum brake.     

Reliability assessment for systems suffering common cause failure based on Bayesian networks and proportional hazards model

The Bayesian network (BN) is an efficient tool for probabilistic modeling and causal inference, and it has gained considerable attentions in the field of reliability assessment. The common cause failure (CCF) is simultaneous failure of multiple elements in a system under a common cause, and it is a common phenomenon in engineering systems with dependent elements. Several models and methods have been proposed for modeling and assessment of complex systems with CCF. In this paper, a new reliability assessment method is proposed for the systems suffering from CCF in a dynamic environment. The CCF among components is characterized by a BN, which allows for bidirectional reasoning. A proportional hazards model is applied to capture the dynamic working environment of components and then the reliability function of the system is obtained. The proposed method is validated through an illustrative example, and some comparative studies are also presented.     

A two-parameter parsimonious bathtub model for the analysis of system failure times with illustrations to reliability data

In this study, a two-parameter, upper-bounded probability distribution called the tau distribution is introduced and its applications in reliability engineering are presented. Each of the parameters of the tau distribution has a clear semantic meaning. Namely, one of them determines the upper bound of the distribution, while the value of the other parameter influences the shape of the cumulative distribution function. A remarkable property of this new probability distribution is that its probability density function, survival function, hazard rate function (HRF), and quantile function can all be expressed in terms of its cumulative distribution function. The HRF of the proposed probability distribution can exhibit an increasing trend and various bathtub shapes with or without a low and long-flat phase (useful time phase), which makes this new distribution suitable for modeling a wide range of real-world problems. The constraint maximum likelihood estimation, percentile estimation, approximate Bayesian computation, and approximate quantile estimation computation are proposed to calculate the unknown parameters of the model. The suitability of the estimation methods is verified with the aid of simulation and real-world data results. The modeling capability of the tau distribution was compared with that of some well-known two- and three-parameter probability distributions using two data sets known from the literature of reliability engineering: time between failures data of a machining center, and time to failure of data acquisition system cards. Based on empirical results, the new distribution may be viewed as a viable competitor to the Weibull, Gamma, Chen, and modified Weibull distributions.     

A new risk prioritization model for failure mode and effects analysis

Failure modes and effects analysis is a framework that has been widely used to improve reliability by prioritizing failures modes using the so‐called risk priority number. However, the risk priority number has some problems frequently pointed out in literature, namely its non‐injectivity, non‐surjectivity, and the impossibility to give weights to risk variables. Despite these disadvantages, the risk priority number continues to be widely used due to its higher simplicity when compared with other alternatives found in literature. In this paper, we propose a novel risk prioritization model to overcome the major drawbacks of the risk priority number. The model contains 2 functions, the risk isosurface function that prioritizes 3 risk variables considering their order of importance in a given risk scenario, and the risk prioritization index function which prioritizes 3 risk variables considering their weights. The novelty of the proposed model is its injectivity, surjectivity, and ease of use in failure modes prioritization. The performance of the proposed model was analyzed using some examples typically used to discuss the conventional risk priority number shortcomings. The model was applied to a case study and its performance correlated with other risk prioritization models. Results show that the failure modes prioritization reached with the proposed model agrees with the expectations made for the risk scenario.     

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

In power and energy systems, both the aerodynamic performance and the structure reliability of turbine equipment are affected by utilized blades. In general, the design process of blade is high dimensional and nonlinear. Different coupled disciplines are also involved during this process. Moreover, unavoidable uncertainties are transported and accumulated between these coupled disciplines, which may cause turbine equipment to be unsafe. In this study, a saddlepoint approximation reliability analysis method is introduced and combined with collaborative optimization method to address the above challenge. During the above reliability analysis and design optimization process, surrogate models are utilized to alleviate the computational burden for uncertainties‐based multidisciplinary design and optimization problems. Smooth response surfaces of the performance of turbine blades are constructed instead of expensively time‐consuming simulations. A turbine blade design problem is solved here to validate the effectiveness and show the utilization of the given approach.     

Different insights for improving part and system reliability obtained from exactly same DFOM “failure numbers”

Techniques for improving the reliability and maintainability of both nonrepairable and repairable items can be suggested by failure data analysis. It is shown that a given set of failure numbers leads to very different improvement strategies when the numbers are the times-between-successive-failures of one or more repairable items, rather than the times-to-failure of nonrepairable items. Since this should have been obvious more than 50 years ago, at the onset of formal reliability engineering activities, several reasons are proffered for the widespread and protracted misinterpretation of even the most basic—and simple!—conceptual and practical differences between nonrepairable and repairable items.     

Reliability qualification of semiconductor devices based on physics‐of‐failure and risk and opportunity assessment

Qualification frequently is a time‐critical activity at the end of a development project. As time‐to‐market is a competitive issue, the most efficient qualification efforts are of interest. A concept is outlined, which proactively integrates qualification into the development process and provides a systematic procedure as a support tool to development and gives early focus on required activities. It converts requirements for a product into measures of development and qualification in combination with a risk and opportunity assessment step and accompanies the development process as a guiding and recording tool for advanced quality planning and confirmation. The collected data enlarge the knowledge database for DFR/BIR (designing for reliability/building‐in reliability) to be used for future projects. The procedure challenges and promotes teamwork of all the disciplines involved. Based on the physics‐of‐failure concept the reliability qualification methodology is re‐arranged with regard to the relationships between design, technology, manufacturing and the different product life phases at use conditions. It makes use of the physics‐of‐failure concept by considering the potential individual failure mechanisms and relates most of the reliability aspects to the technology rather than to the individual product design. Evaluation of complex products using common reliability models and the definition of sample sizes with respect to systematic inherent product properties and fractions of defects are discussed. Copyright © 2002 John Wiley & Sons, Ltd.     

基于云模型的汽车前照灯弯道自适应系统建模与仿真

针对汽车上安装的前照灯具有固定照射范围,当汽车夜间转弯时前照灯无法调节照明角度,常在弯道内侧出现盲区等情况,首先建立了线性二自由度汽车模型、前照灯光轴水平方向调节模型和前照灯步进电机模型,然后提出了基于-维云模型控制的自适应前照灯系统控制算法,建立了汽车弯道行驶自适应前照灯控制系统模型并利用MATLAB进行了仿真.仿真...     

A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators

Operators in nuclear power plants have to acquire information from human system interfaces (HSIs) and the environment in order to create, update, and confirm their understanding of a plant state, as failures of situation assessment may cause wrong decisions for process control and finally errors of commission in nuclear power plants. A few computational models that can be used to predict and quantify the situation awareness of operators have been suggested. However, these models do not sufficiently consider human characteristics for nuclear power plant operators.In this paper, we propose a computational model for situation assessment of nuclear power plant operators using a Bayesian network. This model incorporates human factors significantly affecting operators’ situation assessment, such as attention, working memory decay, and mental model.As this proposed model provides quantitative results of situation assessment and diagnostic performance, we expect that this model can be used in the design and evaluation of human system interfaces as well as the prediction of situation awareness errors in the human reliability analysis.     

Condition-based fault tree analysis (CBFTA): A new method for improved fault tree analysis (FTA), reliability and safety calculations

Condition-based maintenance methods have changed systems reliability in general and individual systems in particular. Yet, this change does not affect system reliability analysis. System fault tree analysis (FTA) is performed during the design phase. It uses components failure rates derived from available sources as handbooks, etc. Condition-based fault tree analysis (CBFTA) starts with the known FTA. Condition monitoring (CM) methods applied to systems (e.g. vibration analysis, oil analysis, electric current analysis, bearing CM, electric motor CM, and so forth) are used to determine updated failure rate values of sensitive components. The CBFTA method accepts updated failure rates and applies them to the FTA. The CBFTA recalculates periodically the top event (TE) failure rate (λ_TE) thus determining the probability of system failure and the probability of successful system operation—i.e. the system's reliability.FTA is a tool for enhancing system reliability during the design stages. But, it has disadvantages, mainly it does not relate to a specific system undergoing maintenance.CBFTA is tool for updating reliability values of a specific system and for calculating the residual life according to the system's monitored conditions. Using CBFTA, the original FTA is ameliorated to a practical tool for use during the system's field life phase, not just during system design phase.This paper describes the CBFTA method and its advantages are demonstrated by an example.