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.     

Comprehensive reliability evaluation of multistate complex electromechanical systems based on similarity of cloud models

High reliability and security have become the hallmarks of complex electromechanical systems. Owing to the difficulties in fault data collection, ambiguity and uncertainty have been inevitably associated with complex electromechanical systems. Thus, the ability to perform reliability evaluation using scarce fault data is of immense significance to these machines and is the focus of this study. A similarity based cloud model is proposed to evaluate the running state of complex electromechanical systems. By combining objective and subjective factors, the reliability of complex electromechanical systems is evaluated by calculating the similarity between the cloud models of actual and standardised states. Next, the inverter of an offshore wind turbine is used to verify the accuracy and effectiveness of the proposed approach. The cloud model based framework for reliability evaluation inherits the preponderance of the uncertainty problem, overcomes the drawbacks of the current reliability approaches, and provides a theoretical basis, as well as a practical approach for the maintenance and repair of complex electromechanical systems with missing fault data. Additionally, it also provides a new methodology for solving the uncertainty problems caused by paucity of data.     

Reliability of safety-critical systems: A state-of-the-art review

Safety-critical systems are designed to prevent catastrophic consequences from failure, such as injury or death to humans and environmental damage. These must be carefully designed to ensure reliability requirements. The purpose of this paper is to identify the number of models in the reliability analysis of safety-critical systems. To achieve this goal, we conducted a systematic review of 40 shortlisted studies. The selected studies are classified based on various techniques used in safety-critical systems. This paper summarizes the literature review in the field of reliability of safety-critical systems. The limitations of the literature are exquisitely represented. The international safety norms and applications of safety-critical systems are discussed systematically in this paper. This paper emerges research trends, research challenges and insights of the researcher for future research direction in the area of safety-critical systems.     

Reliability assessment for a stochastic manufacturing system with reworking actions

This article evaluates the system reliability of a manufacturing system with reworking actions, where the system reliability is an essential indicator to determine whether the manufacturing system is capable or not. Based on the path concept, we transformed the manufacturing system into a stochastic-flow network in which the capacity of each machine is stochastic (i.e., multistate) due to failure, partial failure, and maintenance. In such a manufacturing network, the input flow (raw materials/WIP; work-in-process) processed by each machine might be defective and thus the output flow (WIP/products) would be less than the input amount. To analyze the different sources processed by the manufacturing network, we decomposed the network into one general processing path and several reworking paths by a graphical technique. Subsequently, an algorithm for the manufacturing network was proposed to generate the lower boundary vector which allows sufficient products to satisfy the demand. In terms of such a vector, the system reliability can be derived easily.     

一种面向复杂嵌入式系统的互连网络研究

提出了一种结构简单,具有分形特征的基三分层互连网络.讨论了适合于基三分层网络的节点编码方案,给出了网络中任意两个节点之间最短路径的求解算法和网络平均最短距离的估计值.针对复杂嵌入式系统的特点,分析了基三分层网络的成本、可靠性和实时性.将基三分层网络与常见的嵌入式实时互连拓扑结构进行了比较,结果表明基三分层网络可靠性较高,具有较高的性价比,适合作为通信局部性较高的嵌入式实时互连网络.     

Robust energy-efficient train speed profile optimization in a scenario-based position–time–speed network

Train speed profile optimization is an efficient approach to reducing energy consumption in urban rail transit systems. Different from most existing studies that assume deterministic parameters as model inputs, this paper proposes a robust energy-efficient train speed profile optimization approach by considering the uncertainty of train modeling parameters. Specifically, we first construct a scenario-based position–time–speed (PTS) network by considering resistance parameters as discrete scenario-based random variables. Then, a percentile reliability model is proposed to generate a robust train speed profile, by which the scenario-based energy consumption is less than the model objective value at \alpha confidence level. To solve the model efficiently, we present several algorithms to eliminate the infeasible nodes and arcs in the PTS network and propose a model reformulation strategy to transform the original model into an equivalent linear programming model. Lastly, on the basis of our field test data collected in Beijing metro Yizhuang line, a series of experiments are conducted to verify the effectiveness of the model and analyze the influences of parameter uncertainties on the generated train speed profile.     

Reliability assessment for failure-dependent and uncertain systems: A Bayesian network based on copula method and probability-box

Managing failure dependence of complex systems with hybrid uncertainty is one of the hot problems in reliability assessment. Epistemic uncertainty is attributed to complex working environment, system structure, human factors, imperfect knowledge, etc. Probability-box has powerful characteristics for uncertainty analysis and can be effectively adopted to represent epistemic uncertainty. However, arithmetic rules on probability-box structures are mostly used among structures representing independent random variables. In most practical engineering applications, failure dependence is always introduced in system reliability analysis. Therefore, this paper proposes a developed Bayesian network combining copula method with probability-box for system reliability assessment. There are four main steps involved in the reliability computation process: marginal distribution identification and estimation, copula function selection and parameter estimation, reliability analysis of components with correlations and Bayesian forward analysis. The benefits derived from the proposed approach are used to overcome the computational limitations of n-dimensional integral operation, and the advantages of useful properties of copula function in reliability analysis of systems with correlations are adopted. To demonstrate the effectiveness of the developed Bayesian network, the proposed method is applied to a real large piston compressor.     

Physics-data-fusion based decoupling model for coupled faults of complex electromechanical systems

Coupled faults are formed by the nonlinear coupling of multiple lower-level faults in complex electromechanical systems (CES). Although fault decoupling plays a crucial role in locating fault cause and isolating fault components, it still faces challenges due to the harsh reality of common mode failure, networked propagation, and a lack of accurate fault mechanism knowledge in the fault coupling process. A novel physics-data-fusion-based decoupling model for coupled faults of CES was proposed using standard meta components, rigorous formulation, and intuitive representation. First, a hierarchical graph representing the static complex decoupling model was defined by composing proposed meta models. Second, the dynamic model parameters inspired by the time-varying fault characteristics were determined using real-time operation data analysis. Then, based on a proposed numerical reasoning formula, the most likely fault cause was determined, which can also identify fault level by level. Finally, the decoupling model was proved to be reasonable and effective with an offshore wind turbine case. As a graphical modelling method, it handles the decoupling process by fusing static physics and dynamic data of coupled faults. While inheriting the benefits of conventional models, it overcomes the limitations of these existing methods for real-time results. Moreover, the proposed method provided a foundation for tracing the root cause of performance fluctuations, fault detection, and isolation of CES.     

基于故障危害度的飞机电源系统可靠性评估

基于故障危害度对飞机电源系统进行可靠性评估,首先建立可靠性评估模型,该模型考虑各故障样本对飞机电源系统供电可靠性危害度影响,将出现的故障按照对电源系统安全、性能、任务及维修等指标的影响程度进行等级分类,从故障的失效机理出发建立相应的分布模型;然后采用分布计算和二次分布等算法进行系统整体可靠性指标评估。应用该模型对飞机电源系统进行了故障统计分析,计算了飞机电源系统的可靠性指标。     

Reliability technology using GO methodology: A review

Goal‐oriented (GO) methodology is a graphic inductive method mapping the system structure, its function constitute, characteristics, and working principle visually based on decision tree. Recently, the studies on reliability technologies for GO method of complex systems have been gotten a lot of attention by scholars because of the obvious advantages of modeling and analysis power. Indeed, the GO method has been applied in reliability and safety analysis of various areas, such as transportation and power systems, to prove its value. Thus, this paper provides a brief review of the GO method from aspects of providing its development history and its engineering applications and expounding its development of the theory of the GO method. At last, the future directions of GO method are discussed.     

网络中心战条件下舰艇编队武器系统可靠度分析

利用可靠度模型对网络化条件下舰艇编队跨平台组织武器通道的可靠度进行了分析。分析表明，在网络中心战条件下舰艇编队武器系统的可靠度有了大幅度的提升。     

贝叶斯网络推理在信息系统安全风险评估中的应用

提出了一种基于贝叶斯网络推理的安全风险评估方法。从实际出发建立信息系统的贝叶斯网络模型，根据专家给出的先验信息，结合获得的证据信息，运用Pearl方法完成对模型的评估，给出在特定条件下模型的计算——线性推理算法。最后，以实例分析信息系统安全评估的实现过程，结果表明，该方法可行、有效。     

Reliability analysis for multidisciplinary systems with the mixture of epistemic and aleatory uncertainties

Epistemic and aleatory uncertain variables always exist in multidisciplinary system simultaneously and can be modeled by probability and evidence theories, respectively. The propagation of uncertainty through coupled subsystem and the strong nonlinearity of the multidisciplinary system make the reliability analysis difficult and computational cost expensive. In this paper, a novel reliability analysis procedure is proposed for multidisciplinary system with epistemic and aleatory uncertain variables. First, the probability density function of the aleatory variables is assumed piecewise uniform distribution based on Bayes method, and approximate most probability point is solved by equivalent normalization method. Then, important sampling method is used to calculate failure probability and its variance and variation coefficient. The effectiveness of the procedure is demonstrated by two numerical examples. Copyright © 2013 John Wiley & Sons, Ltd.     

Reliability modeling of multi‐state degraded repairable systems and its applications to automotive systems

In this paper, we presented a continuous‐time Markov process‐based model for evaluating time‐dependent reliability indices of multi‐state degraded systems, particularly for some automotive subsystems and components subject to minimal repairs and negative repair effects. The minimal repair policy, which restores the system back to an “as bad as old” functioning state just before failure, is widely used for automotive systems repair because of its low cost of maintenance. The current study distinguishes with others that the negative repair effects, such as unpredictable human error during repair work and negative effects caused by propagated failures, are considered in the model. The negative repair effects may transfer the system to a degraded operational state that is worse than before due to an imperfect repair. Additionally, a special condition that a system under repair may be directly transferred to a complete failure state is also considered. Using the continuous‐time Markov process approach, we obtained the general solutions to the time‐dependent probabilities of each system state. Moreover, we also provided the expressions for several reliability measures include availability, unavailability, reliability, mean life time, and mean time to first failure. An illustrative numerical example of reliability assessment of an electric car battery system is provided. Finally, we use the proposed multi‐state system model to model a vehicle sub‐frame fatigue degradation process. The proposed model can be applied for many practical systems, especially for the systems that are designed with finite service life.     

Reliability assessment of locking systems

The aim of this work is to predict the failure probability of a locking system. This failure probability is assessed using complementary methods: the First-Order Reliability Method (FORM) and Second-Order Reliability Method (SORM) as approximated methods, and Monte Carlo simulations as the reference method. Both types are implemented in a specific software [Phimeca software. Software for reliability analysis developed by Phimeca Engineering S.A.] used in this study. For the Monte Carlo simulations, a response surface, based on experimental design and finite element calculations [Abaqus/Standard User’s Manuel vol. I.], is elaborated so that the relation between the random input variables and structural responses could be established. Investigations of previous reliable methods on two configurations of the locking system show the large sturdiness of the first one and enable design improvements for the second one.     

A complex network approach to environmental impact assessment

This article proposes a complex network methodology for the process of Environmental Impact Assessment (EIA) that limits subjectivity and reduces uncertainty by incorporating elements of complex systems theory in the stages of identification and assessment of the significance of environmental impacts. The proposed methodology reduces the sources of uncertainty, which emerge from the use of simplified models that analyse the environment-activity interactions in a unidirectional fashion. This proposal determines the significance of environmental impacts through multidirectional or complex causal relationships. Likewise, it limits the subjectivity of the evaluator by using these causality relationships instead of criteria based on the impacts’ attributes. The application of the proposed methodology demonstrates the advantages of (i) prioritizing the impacts according to their capacity to interact with other impacts, and (ii) the possibility to redirect the environmental management plans towards the prevention of impacts of higher complexity and to reduce the importance of derived impacts.

The application of the proposed methodology reveals that the percentage of irrelevant and moderate impacts is reduced, whereas the percentage of severe and critical impacts increase, in comparison to the conventional methodologies.     

部分信息下相邻2-out-of-n:F系统的可靠性分析

对相邻2-out-of-n系统可靠性的研究大都假定部件的失效时间分布是已知的,即部件的可靠度是已知的,然而该假设在许多情况下是不合理的(例如软件系统,人机系统)。本文针对部件的失效时间分布未知,仅仅具有关于部件可靠度的部分信息情况,也就是我们仅仅知道部件的寿命在某些重叠区间的可靠度,利用不精确概率理论分析了该系统,获得了相邻2-out-of-n:F环形与线形系统可靠度上下界的精确公式。最后用例子说明了本文的方法,并指出了在该方向上需要进一步研究解决的问题。     

Reliability modeling for multistage systems subject to competing failure processes

In this paper, a novel multistage reliability model is provided as systems are often divided into many stages according to system degradation characteristics. Multistage hard failure (caused by random shock) process (MHFP) and multistage soft failure (caused by random shock and continuous degradation) process (MSFP) are introduced to describe the competing failure processes, where either the MSFP or MHFP would break down the system. The shock processes impact the system in three ways: (1) fatal load shocks cause hard failure immediately in the hard failure process; (2) time shocks cause a hard failure threshold changing; (3) damage load shocks cause degradation level increasing in the soft failure process. In this paper, a density function dispersion method is carried out to address the multistage reliability model, and the effectiveness of the proposed models is demonstrated by reliability analysis with the one-stage model. Finally, the multistage model is applied to a case study, the degradation process is divided into three stages, and the hard failure threshold can be transmitted twice. The proposed model can be applied in other multistage situations, and the calculation method can satisfy the accuracy requirements.