Forward and backward risk assessment throughout a system life cycle using dynamic Bayesian networks: A case in a petroleum refinery

In this paper, risk modeling was conducted based on the defined risk elements of a conceptual risk framework. This model allows for the estimation of a variety of risks, including human error probability, operational risk, financial risk, technological risk, commercial risk, health risk, and social and environmental risks. Bayesian network (BN) structure learning techniques were used to determine the relationships among the model variables. By solving a bi-objective optimization problem applying the genetic algorithm (GA) with the Pareto ranking approach, the network structure was learned. Then, risk modeling was performed for a petroleum refinery focusing on HydroDeSulfurization (HDS) technology throughout its life cycle. To extend the model horizontally and make it possible to evaluate the risk trend throughout the technology life cycle, we developed a dynamic Bayesian network (DBN) with three-time slices. A two-way forward and backward approach was used to analyze the model. The model validation was performed by applying the leave-one-out cross-validation method.     

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。     

Bayesian networks for multilevel system reliability

Bayesian networks have recently found many applications in systems reliability; however, the focus has been on binary outcomes. In this paper we extend their use to multilevel discrete data and discuss how to make joint inference about all of the nodes in the network. These methods are applicable when system structures are too complex to be represented by fault trees. The methods are illustrated through four examples that are structured to clarify the scope of the problem.     

Risk-based reconfiguration of safety monitoring system using dynamic Bayesian network

To prevent an abnormal event from leading to an accident, the role of its safety monitoring system is very important. The safety monitoring system detects symptoms of an abnormal event to mitigate its effect at its early stage. As the operation time passes by, the sensor reliability decreases, which implies that the decision criteria of the safety monitoring system should be modified depending on the sensor reliability as well as the system reliability. This paper presents a framework for the decision criteria (or diagnosis logic) of the safety monitoring system. The logic can be dynamically modified based on sensor output data monitored at regular intervals to minimize the expected loss caused by two types of safety monitoring system failure events: failed-dangerous (FD) and failed-safe (FS). The former corresponds to no response under an abnormal system condition, while the latter implies a spurious activation under a normal system condition. Dynamic Bayesian network theory can be applied to modeling the entire system behavior composed of the system and its safety monitoring system. Using the estimated state probabilities, the optimal decision criterion is given to obtain the optimal diagnosis logic. An illustrative example of a three-sensor system shows the merits and characteristics of the proposed method, where the reasonable interpretation of sensor data can be obtained.     

Risk assessment on the EA-6B aircraft utilizing Bayesian networks

ABSTRACT

Military weapon systems often remain in service longer than anticipated. Systems must continue to operate safety and effectively while maintaining mission readiness. Degraders to readiness, such as high failure items, excessive repair times, long logistics delays, and manpower shortfalls, must be anticipated in order to proactively reduce risk. We applied a Bayesian network to a field data set obtained from the U.S. military. Our approach yielded a predictive method with substantial benefits over reactive methods, and was able to predict failure of several important components, to include potential malfunction codes.     

Dynamic risk assessment of complex systems using FCM

Analysing risk of today’s complex systems is challenging due to the complex and dynamic nature of systems. The current risk analysis tools are not able to take the complex interactions among risks into account and therefore they can’t predict the behaviour of risks accurately. In an attempt to overcome this shortcoming, this paper proposes an integrated generalised decision support tool using fuzzy cognitive maps for dynamic risk assessment of complex systems. The proposed approach has the ability to prioritise risk factors and more importantly predict and analysis the influences of each individual risk factor/risk set on the other risks or on the outcomes of complex and critical systems by taking into account probability of occurrence and consequences of risks and also considering the complex dependencies between risk factors. These features could provide practitioners with realistic results in critical industries and able them to manage risks more efficiently.     

Bayesian belief networks for system fault diagnostics

Fault diagnostic methods aim to recognize when faults exist on a system and to identify the failures that have caused the fault. The symptoms of the fault are obtained from readings from sensors located on the system. When the observed readings do not match those expected then a fault can exist. Using the detailed information provided by the sensors, a list of the failures (singly or in combinations) that could cause the symptoms can be deduced. In the last two decades, fault diagnosis has received growing attention due to the complexity of modern systems and the consequent need for more sophisticated techniques to identify the failures when they occur. Detecting the causes of a fault quickly and efficiently means reducing the costs associated with the system unavailability and, in certain cases, avoiding the risks of unsafe operating conditions. Bayesian belief networks (BBNs) are probabilistic models that were developed in artificial intelligence applications but are now applied in many fields. They are ideal for modelling the causal relations between faults and symptoms used in the detection process. The probabilities of events within the BBN can be updated following observations (evidence) about the system state. In this paper we investigate how BBNs can be applied to diagnose faults on a system. Initially Fault trees (FTs) are constructed to indicate how the component failures can combine to cause unexpected deviations in the variables monitored by the sensors. Converting FTs into BNs enables the creation of a model that represents the system with a single network, which is constituted by sub‐networks. The posterior probabilities of the components' failures give a measure of those components that have caused the symptoms observed. The method gives a procedure that can be generalized for any system where the causality structure can be developed relating the system component states to the sensor readings. The technique is demonstrated with a simple example system. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal

The scenario in a risk analysis can be defined as the propagating feature of specific initiating event which can go to a wide range of undesirable consequences. If we take various scenarios into consideration, the risk analysis becomes more complex than do without them. A lot of risk analyses have been performed to actually estimate a risk profile under both uncertain future states of hazard sources and undesirable scenarios. Unfortunately, in case of considering specific systems such as a radioactive waste disposal facility, since the behaviour of future scenarios is hardly predicted without special reasoning process, we cannot estimate their risk only with a traditional risk analysis methodology. Moreover, we believe that the sources of uncertainty at future states can be reduced pertinently by setting up dependency relationships interrelating geological, hydrological, and ecological aspects of the site with all the scenarios. It is then required current methodology of uncertainty analysis of the waste disposal facility be revisited under this belief.In order to consider the effects predicting from an evolution of environmental conditions of waste disposal facilities, this paper proposes a quantitative assessment framework integrating the inference process of Bayesian network to the traditional probabilistic risk analysis. We developed and verified an approximate probabilistic inference program for the specific Bayesian network using a bounded-variance likelihood weighting algorithm. Ultimately, specific models, including a model for uncertainty propagation of relevant parameters were developed with a comparison of variable-specific effects due to the occurrence of diverse altered evolution scenarios (AESs). After providing supporting information to get a variety of quantitative expectations about the dependency relationship between domain variables and AESs, we could connect the results of probabilistic inference from the Bayesian network with the consequence evaluation model addressed. We got a number of practical results to improve current knowledge base for the prioritization of future risk-dominant variables in an actual site.     

Sensitivity analysis in Gaussian Bayesian networks using a symbolic-numerical technique

The paper discusses the problem of sensitivity analysis in Gaussian Bayesian networks. The algebraic structure of the conditional means and variances, as rational functions involving linear and quadratic functions of the parameters, are used to simplify the sensitivity analysis. In particular the probabilities of conditional variables exceeding given values and related probabilities are analyzed. Two examples of application are used to illustrate all the concepts and methods.     

基于贝叶斯网络的核电站应急电力系统安全评价

在故障树分析法(FTA)基础上提出了一种基于贝叶斯网络(BN)的核电站应急电力系统安全评价方法,比较了FTA和BN在建立安全评价模型和评价能力上的不同.该方法在应对众多影响因素上有很大优势,能进行更多有意义的分析:既能进行前向的预测推理,又能进行后向的诊断推理,可以找出影响故障的组合模式,从而能够找出系统的薄弱环节.同时采用基于Matlab的BNT软件包,大大简化了计算过程.通过对10MW高温气冷堆(HTR-10)应急电力系统的安全评价实例的分析,证明该方法是对传统的基于故障树分析的安全评价方法的有益改进.     

Bayesian network modelling for supply chain risk propagation

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.     

Improving the analysis of dependable systems by mapping fault trees into Bayesian networks

Bayesian Networks (BN) provide a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks but they have received little attention in the area of dependability. The present paper is aimed at exploring the capabilities of the BN formalism in the analysis of dependable systems. To this end, the paper compares BN with one of the most popular techniques for dependability analysis of large, safety critical systems, namely Fault Trees (FT). The paper shows that any FT can be directly mapped into a BN and that basic inference techniques on the latter may be used to obtain classical parameters computed from the former (i.e. reliability of the Top Event or of any sub-system, criticality of components, etc). Moreover, by using BN, some additional power can be obtained, both at the modeling and at the analysis level. At the modeling level, several restrictive assumptions implicit in the FT methodology can be removed and various kinds of dependencies among components can be accommodated. At the analysis level, a general diagnostic analysis can be performed. The comparison of the two methodologies is carried out by means of a running example, taken from the literature, that consists of a redundant multiprocessor system.     

Forensic assessment of a bridge downfall using Bayesian networks

Bayesian networks proved to be a useful tool in many technical fields as well as in forensic sciences. The present paper proposes a novel application of Bayesian networks in forensic engineering, focusing on the analysis of technical causes of a catastrophic bridge downfall. During repair a road bridge over important railway lines suddenly slipped down from temporary supports. Incidentally at the same time an intercity train approached the location and crashed into the collapsed bridge at a high speed. The accident resulted in great societal and economic consequences. Forensic investigation concerning causes of the bridge collapse was complicated due to the additional damage caused by the train. Moreover, the remaining structural elements of the collapsed bridge and temporary supports were shortly after the accident removed to renew railway traffic. Background materials of the investigation and additional detailed structural analyses did not reveal any convincing evidence of the initiation cause. Critical consideration of all possible causes including aerodynamic effects supplemented by a causal (Bayesian) network finally resulted in identification of the most significant causes including insufficient foundation and overall stiffness of temporary supports.     

Bayesian-network-based safety risk assessment for steel construction projects

There are four primary accident types at steel building construction (SC) projects: falls (tumbles), object falls, object collapse, and electrocution. Several systematic safety risk assessment approaches, such as fault tree analysis (FTA) and failure mode and effect criticality analysis (FMECA), have been used to evaluate safety risks at SC projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. To overcome the limitations of traditional approaches, this study addresses the development of a safety risk-assessment model for SC projects by establishing the Bayesian networks (BN) based on fault tree (FT) transformation. The BN-based safety risk-assessment model was validated against the safety inspection records of six SC building projects and nine projects in which site accidents occurred. The ranks of posterior probabilities from the BN model were highly consistent with the accidents that occurred at each project site. The model accurately provides site safety-management abilities by calculating the probabilities of safety risks and further analyzing the causes of accidents based on their relationships in BNs. In practice, based on the analysis of accident risks and significant safety factors, proper preventive safety management strategies can be established to reduce the occurrence of accidents on SC sites.     

基于贝叶斯网络的计算机网络端到端服务故障诊断

研究了利用贝叶斯网络不确定推理技术实现端到端服务故障诊断的方法，详细描述了贝叶斯网络故障诊断模型的建立方法，设计了基于Pearl信念传播机制的故障诊断算法，并对其进行了改进，以提高诊断效果．最后，通过仿真验证了该方法的有效性，并提出了下一步的研究方向．     

Knowledge discovery from observational data for process control using causal Bayesian networks

This paper investigates learning causal relationships from the extensive datasets that are becoming increasingly available in manufacturing systems. A causal modeling approach is proposed to improve an existing causal discovery algorithm by integrating manufacturing domain knowledge with the algorithm. The approach is demonstrated by discovering the causal relationships among the product quality and process variables in a rolling process. When allied with engineering interpretations, the results can be used to facilitate rolling process control.     

Modeling of latent structure of indomethacin solid dispersion tablet using Bayesian networks

Background: When designing pharmaceutical products, the relationships between causal factors and pharmaceutical responses are intricate. A Bayesian network (BN) was used to clarify the latent structure underlying the causal factors and pharmaceutical responses of a tablet containing solid dispersion (SD) of indomethacin (IMC).

Method: IMC, a poorly water-soluble drug, was tested with polyvinylpyrrolidone as the carrier polymer. Tablets containing a SD or a physical mixture of IMC, different quantities of magnesium stearate, microcrystalline cellulose, and low-substituted hydroxypropyl cellulose, and subjected to different compression force were selected as the causal factors. The pharmaceutical responses were the dissolution properties and tensile strength before and after the accelerated test and a similarity factor, which was used as an index of the storage stability.

Result: BN models were constructed based on three measurement criteria for the appropriateness of the graph structure. Of these, the BN model based on Akaike’s information criterion was similar to the results for the analysis of variance. To quantitatively estimate the causal relationships underlying the latent structure in this system, conditional probability distributions were inferred from the BN model. The responses were accurately predicted using the BN model, as reflected in the high correlation coefficients in a leave-one-out cross-validation procedure.

Conclusion: The BN technique provides a better understanding of the latent structure underlying causal factors and responses.     

Leakage detection in water pipe networks using a Bayesian probabilistic framework

A Bayesian system identification methodology is proposed for leakage detection in water pipe networks. The methodology properly handles the unavoidable uncertainties in measurement and modeling errors. Based on information from flow test data, it provides estimates of the most probable leakage events (magnitude and location of leakage) and the uncertainties in such estimates. The effectiveness of the proposed framework is illustrated by applying the leakage detection approach to a specific water pipe network. Several important issues are addressed, including the role of modeling error, measurement noise, leakage severity and sensor configuration (location and type of sensors) on the reliability of the leakage detection methodology. The present algorithm may be incorporated into an integrated maintenance network strategy plan based on computer-aided decision-making tools.     

Modeling and analysis of disease and risk factors through learning Bayesian networks from observational data

This paper focuses on identification of the relationships between a disease and its potential risk factors using Bayesian networks in an epidemiologic study, with the emphasis on integrating medical domain knowledge and statistical data analysis. An integrated approach is developed to identify the risk factors associated with patients' occupational histories and is demonstrated using real‐world data. This approach includes several steps. First, raw data are preprocessed into a format that is acceptable to the learning algorithms of Bayesian networks. Some important considerations are discussed to address the uniqueness of the data and the challenges of the learning. Second, a Bayesian network is learned from the preprocessed data set by integrating medical domain knowledge and generic learning algorithms. Third, the relationships revealed by the Bayesian network are used for risk factor analysis, including identification of a group of people who share certain common characteristics and have a relatively high probability of developing the disease, and prediction of a person's risk of developing the disease given information on his/her occupational history. Copyright © 2007 John Wiley & Sons, Ltd.