A discrete-time Bayesian network reliability modeling and analysis framework

Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel reliability modeling and analysis framework based on the Bayesian network (BN) formalism. The overall approach is to investigate timed Bayesian networks and to find a suitable reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis.     

Probabilistic reliability assessment of a heritage structure under horizontal loads

This paper proposes a methodology for the probabilistic reliability assessment of heritage buildings. The procedure addresses investigation and tests on the structure and it considers the implementation of Bayesian updating techniques for a rational use of the collected information. After having described the peculiarities of ancient buildings, it is shown how probabilistic methods can be adapted to evaluate their safety. A practical application of the methodology to a relevant case study is presented, namely a historic aqueduct in Italy. The main goal is to demonstrate the effectiveness of a probabilistic approach to the reliability assessment of heritage structures.     

Review of advances in human reliability analysis of errors of commission, Part 1: EOC identification

In close connection with examples relevant to contemporary probabilistic safety assessment (PSA), a review of advances in human reliability analysis (HRA) of post-initiator errors of commission (EOCs), i.e. inappropriate actions under abnormal operating conditions, has been carried out. The review comprises both EOC identification (part 1) and quantification (part 2); part 1 is presented in this article. Emerging HRA methods addressing the problem of EOC identification are: A Technique for Human Event Analysis (ATHEANA), the EOC HRA method developed by Gesellschaft für Anlagen- und Reaktorsicherheit (GRS), the Misdiagnosis Tree Analysis (MDTA) method, and the Commission Errors Search and Assessment (CESA) method. Most of the EOCs referred to in predictive studies comprise the stop of running or the inhibition of anticipated functions; a few comprise the start of a function. The CESA search scheme—which proceeds from possible operator actions to the affected systems to scenarios and uses procedures and importance measures as key sources of input information—provides a formalized way for identifying relatively important scenarios with EOC opportunities. In the implementation however, attention should be paid regarding EOCs associated with familiar but non-procedural actions and EOCs leading to failures of manually initiated safety functions.     

变流量空调水系统稳定性的分析及试验研究

为了探讨压差控制的稳定性,提高该控制方式的节能效果,通过水力稳定度的计算,阐述了水力稳定度对水系统稳定性关系,并讨论了变流量空调水系统中压差设定值及定压点位置对各水力稳定度的影响.最后得出结论:定压点越靠近循环泵,系统总体稳定性越好,但这却降低了水泵的节能效果,压差设定值大小对水力稳定度没有影响.     

Review of advances in human reliability analysis of errors of commission—Part 2: EOC quantification

In close connection with examples relevant to contemporary probabilistic safety assessment (PSA), a review of advances in human reliability analysis (HRA) of post-initiator errors of commission (EOCs), i.e. inappropriate actions under abnormal operating conditions, has been carried out. The review comprises both EOC identification (part 1) and quantification (part 2); part 2 is presented in this article. Emerging HRA methods in this field are: ATHEANA, MERMOS, the EOC HRA method developed by Gesellschaft für Anlagen- und Reaktorsicherheit (GRS), the MDTA method and CREAM. The essential advanced features are on the conceptual side, especially to envisage the modeling of multiple contexts for an EOC to be quantified (ATHEANA, MERMOS and MDTA), in order to explicitly address adverse conditions. There is promising progress in providing systematic guidance to better account for cognitive demands and tendencies (GRS, CREAM), and EOC recovery (MDTA). Problematic issues are associated with the implementation of multiple context modeling and the assessment of context-specific error probabilities. Approaches for task or error opportunity scaling (CREAM, GRS) and the concept of reference cases (ATHEANA outlook) provide promising orientations for achieving progress towards data-based quantification. Further development work is needed and should be carried out in close connection with large-scale applications of existing approaches.     

Assessment of human reliability based on evaluation of plant experience: requirements and implementation

A major problem in assessment of human failures in probabilistic safety assessment is the lack of empirical data needed for human reliability analysis (HRA). This problem is aggravated by the fact that different HRA methods use different parameters for the assessment and that HRA is currently enforced to provide data and methods for assessment of human reliability in new technical environments such as computerized control rooms, in accident management situations, or in low-power and shut down situations. Plant experience is one source to deal with this problem. In this paper, a method is presented that describes how plant experience about human failures and human performance may be used to support the process of analyzing and assessing human reliability. Based on considerations of requirements of HRA, a method is presented first which is able to describe and analyze human interactions that were observed within events. Implementation of the approach as a database application is outlined. Second, the main results of the application of the method to 165 boiling water reactor events are presented. Observed influencing factors on human performance are discussed; estimates for probabilities are calculated and compared with the data tables of the THERP handbook. An outline is given for using the presented method for the analysis of cognitive errors or organizational aspects.     

Dynamic response and reliability analysis of non-linear stochastic structures

A new probability density evolution method is proposed for dynamic response analysis and reliability assessment of non-linear stochastic structures. In the method, a completely uncoupled one-dimensional governing partial differential equation is derived first with regard to evolutionary probability density function (PDF) of the stochastic structural responses. This equation holds for any response or index of the structure. The solution will put out the instantaneous PDF. From the standpoint of the probability transition process, the reliability of the structure is evaluated in a straightforward way by imposing an absorbing boundary condition on the governing PDF equation. However, this does not induce additional computational efforts compared with the dynamic response analysis. The computational algorithm to solve the PDF equation is studied. A deterministic dynamic response analysis procedure is embedded to compute coefficient of the evolutionary PDF equation, which is then numerically solved by the finite difference method with total variation diminishing scheme. It is found that the proposed hybrid algorithm may deal with non-linear stochastic response analysis problem with high accuracy. Numerical examples are investigated. Parts of the results are illustrated. Some features of the probabilistic information of the response and the reliability are observed and discussed. The comparisons with the Monte Carlo simulations demonstrate the accuracy and efficiency of the proposed method.     

Integrated system identification and reliability evaluation of stochastic building structures

System identification and reliability evaluation play a significant role in structural health monitoring to ensure the serviceability and safety of existing structures. Although the development of system identification methods has attained much attention and some degree of maturity, reliability evaluation of existing structures still remains a challenging problem especially when uncertainties in measurement data and inherent randomness, which are inevitably involved in civil structures, are considered. In this regard, this paper presents a framework for integrated system identification and reliability evaluation of stochastic building structures. Two algorithms are proposed to respectively evaluate component reliability and system reliability of stochastic building structures by combining a statistical moment-based system identification method and a probability density evolution equation-based reliability evaluation method. System identification is embedded in the procedure of reliability evaluation of a stochastic building structure. The uncertainties in both the structure and the external excitation are considered. Numerical examples show that the structural component and system reliabilities of a three-story shear building structure with three damage scenarios can be effectively evaluated by the proposed methods.     

转子系统振动的频率可靠性灵敏度分析

摘要：根据转子系统的固有频率与激振频率差的绝对值不超过规定值的关系准则,定义转子系统共振问题的可靠性模式和系统的可靠度,提出避免转子共振的频率可靠性分析方法,应用随机摄动技术、可靠性理论和灵敏度技术,对转子系统共振问题的可靠性灵敏度分析方法进行了研究,并通过数值算例验证了本文方法的有效性。     

The human error rate assessment and optimizing system HEROS — a new procedure for evaluating and optimizing the man–machine interface in PSA

A new procedure allowing the probabilistic evaluation and optimization of the man–machine system is presented. This procedure and the resulting expert system HEROS, which is an acronym for Human Error Rate Assessment and Optimizing System, is based on the fuzzy set theory. Most of the well-known procedures employed for the probabilistic evaluation of human factors involve the use of vague linguistic statements on performance shaping factors to select and to modify basic human error probabilities from the associated databases. This implies a large portion of subjectivity. Vague statements are expressed here in terms of fuzzy numbers or intervals which allow mathematical operations to be performed on them. A model of the man–machine system is the basis of the procedure. A fuzzy rule-based expert system was derived from ergonomic and psychological studies. Hence, it does not rely on a database, whose transferability to situations different from its origin is questionable. In this way, subjective elements are eliminated to a large extent. HEROS facilitates the importance analysis for the evaluation of human factors, which is necessary for optimizing the man–machine system. HEROS is applied to the analysis of a simple diagnosis of task of the operating personnel in a nuclear power plant.     

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.     

Mechanical system reliability analysis using a combination of graph theory and Boolean function

A new method based on graph theory and Boolean function for assessing reliability of mechanical systems is proposed. The procedure for this approach consists of two parts. By using the graph theory, the formula for the reliability of a mechanical system that considers the interrelations of subsystems or components is generated. Use of the Boolean function to examine the failure interactions of two particular elements of the system, followed with demonstrations of how to incorporate such failure dependencies into the analysis of larger systems, a constructive algorithm for quantifying the genuine interconnections between the subsystems or components is provided. The combination of graph theory and Boolean function provides an effective way to evaluate the reliability of a large, complex mechanical system. A numerical example demonstrates that this method an effective approaches in system reliability analysis.     

A polynomial dimensional decomposition framework based on topology derivatives for stochastic topology sensitivity analysis of high-dimensional complex systems and a type of benchmark problems

In this paper, a new computational framework based on the topology derivative concept is presented for evaluating stochastic topological sensitivities of complex systems. The proposed framework, designed for dealing with high dimensional random inputs, dovetails a polynomial dimensional decomposition (PDD) of multivariate stochastic response functions and deterministic topology derivatives. On one hand, it provides analytical expressions to calculate topology sensitivities of the first three stochastic moments which are often required in robust topology optimization (RTO). On another hand, it offers embedded Monte Carlo Simulation (MCS) and finite difference formulations to estimate topology sensitivities of failure probability for reliability-based topology optimization (RBTO). For both cases, the quantification of uncertainties and their topology sensitivities are determined concurrently from a single stochastic analysis. Moreover, an original example of two random variables is developed for the first time to obtain analytical solutions for topology sensitivity of moments and failure probability. Another 53-dimension example is constructed for analytical solutions of topology sensitivity of moments and semi-analytical solutions of topology sensitivity of failure probabilities in order to verify the accuracy and efficiency of the proposed method for high-dimensional scenarios. Those examples are new and make it possible for researchers to benchmark stochastic topology sensitivities of existing or new algorithms. In addition, it is unveiled that under certain conditions the proposed method achieves better accuracies for stochastic topology sensitivities than for the stochastic quantities themselves.     

Comparison of global sensitivity analysis techniques and importance measures in PSA

This paper discusses application and results of global sensitivity analysis techniques to probabilistic safety assessment (PSA) models, and their comparison to importance measures. This comparison allows one to understand whether PSA elements that are important to the risk, as revealed by importance measures, are also important contributors to the model uncertainty, as revealed by global sensitivity analysis. We show that, due to epistemic dependence, uncertainty and global sensitivity analysis of PSA models must be performed at the parameter level. A difficulty arises, since standard codes produce the calculations at the basic event level. We discuss both the indirect comparison through importance measures computed for basic events, and the direct comparison performed using the differential importance measure and the Fussell–Vesely importance at the parameter level. Results are discussed for the large LLOCA sequence of the advanced test reactor PSA.     

The reliability analysis of balking and reneging in a repairable system with warm standbys

We study the reliability analysis of a repairable system with operating units, warm standby units and repairmen in which the balking and reneging of units are considered. It is assumed that failed units balk (refuse to join) with a constant probability ( ) and renege (leave the queue after joining) according to a negative exponential distribution with parameter . The failure times of the operating unit and of a standby unit are assumed to be exponentially distributed with parameters λ and α, respectively. The repair time distribution is also exponential. The repairable system is out of , in which . The reliability characteristics, such as the system reliability and the mean time to system failure (MTTF), in a repairable system are derived. Several cases are analysed graphically to study the effect of various parameters on the reliability and MTTF of the system. Copyright © 2002 John Wiley & Sons, Ltd.     

Architectural design and reliability analysis of a fail-operational brake-by-wire system from ISO 26262 perspectives

Next generation drive-by-wire automotive systems enabling autonomous driving will build on the fail-operational capabilities of electronics, control and software (ECS) architectural solutions. Developing such architectural designs that would meet dependability requirements and satisfy other system constraints is a challenging task and will possibly lead to a paradigm shift in automotive ECS architecture design and development activities. This aspect is becoming quite relevant while designing battery-driven electric vehicles with integrated in-wheel drive-train and chassis subsystems.In such highly integrated dependable systems, many of the primary features and functions are attributed to the highest safety critical ratings. Brake-by-wire is one such system that interfaces with active safety features built into an automobile, and which in turn is expected to provide fail-operational capabilities. In this paper, building up on the basic concepts of fail-silent and fail-operational systems design we propose a system-architecture for a brake-by-wire system with fail-operational capabilities. The design choices are supported with proper rationale and design trade-offs. Safety and reliability analysis of the proposed system architecture is performed as per the ISO 26262 standard for functional safety of electrical/electronic systems in road vehicles.     

充液管道模态的参数灵敏度及其共振可靠性分析

分析充液管道模态的参数灵敏度,并对充液管道的共振可靠性进行了研究。首先考虑管道与液体之间的耦合作用,利用有限元软件对所建立的简单管道系统进行模态计算。然后假设管道结构参数服从某种随机分布,分析了对应的一、二阶模态的概率分布并讨论了这些参数对管道固有频率的灵敏度影响。最后利用干涉理论对流固耦合管道系统进行了防共振可靠性分析。该研究结果可为管道防共振设计和管道的共振可靠性评估提供了参考。     

Identification and classification of dynamic event tree scenarios via possibilistic clustering: Application to a steam generator tube rupture event

This paper illustrates a method to identify and classify scenarios generated in a dynamic event tree (DET) analysis. Identification and classification are carried out by means of an evolutionary possibilistic fuzzy C-means clustering algorithm which takes into account not only the final system states but also the timing of the events and the process evolution. An application is considered with regards to the scenarios generated following a steam generator tube rupture in a nuclear power plant. The scenarios are generated by the accident dynamic simulator (ADS), coupled to a RELAP code that simulates the thermo-hydraulic behavior of the plant and to an operators’ crew model, which simulates their cognitive and procedures-guided responses.A set of 60 scenarios has been generated by the ADS DET tool. The classification approach has grouped the 60 scenarios into 4 classes of dominant scenarios, one of which was not anticipated a priori but was “discovered” by the classifier. The proposed approach may be considered as a first effort towards the application of identification and classification approaches to scenarios post-processing for real-scale dynamic safety assessments.     

Slowing and storing light processes without a trapping dark state in a double two-level system. Theoretical study

We present a detailed theoretical study of a method for slowing light based on the oscillations of Zeeman coherences in a double two-level system [Hashmi, F.A.; Bouchene, M.A. Phys. Rev. A 2008, 77, 051803(R)]. This method does not require the presence of any trapping state. We focus on the properties and the limitations of such a method and compare it to previous ones. We also investigate the possibility of obtaining fast light and to store the light with such a method.