基于贝叶斯网络的机械安全性评估模型研究

重点阐述了基于贝叶斯网络的机械安全性评估模型的建立过程,应用贝叶斯网络建立了开式压力机安全性评估模型,对冲手事故模型中的共因失效节点进行了说明,并最终计算得出开式压力机冲手事故的发生概率.计算结果分析表明:对于系统基本事件之间有共因和相关关系的系统,BN计算结果使得事故后果概率明显增大,设计者和管理者不能忽略共因和相关关系对系统风险的影响.     

基于FBN地铁深基坑施工渗漏风险评估模型及应用

为了解决由于地铁深基坑施工风险因素不确定性和基坑工程事故资料缺失导致传统风险分析方法不再满足实际需要的问题。探讨模糊集理论(FST)和贝叶斯网络(BN)的结合,介绍1种专家置信度指标,建立地铁深基坑渗漏风险评估指标体系,得到基于模糊贝叶斯网络(FBN)地铁深基坑施工渗漏风险评估模型。研究结果表明：将该方法应用于广州地铁十三号线某车站深基坑施工渗漏风险评估中,结果符合施工实际,接缝密封质量差等风险因素需加以措施控制,该方法可为后续施工风险评估提供实时支持。     

Resilience assessment of chemical industrial areas during Natech-related cascading multi-hazards

In chemical industrial areas, technological accidents triggered by natural events (Natech events) may escalate. Complex cascading multi-hazard scenarios with high uncertainties may be caused. Resilience is an essential property of a system to withstand and recover from disruptive events. The present study focuses on the change of the resilience level due to (possible) interactions between cascading hazards, chemical installations and safety barriers during the dynamic evolution of fire escalations triggered by a natural hazard (certain cascading multi-hazard scenarios). A quantitative resilience assessment method is developed to this end. The state transition of a system facing accidents in the context of resilience is explored. Moreover, the uncertainties accompanying an accident evolution are quantified using a Dynamic Bayesian Network, allowing a detailed analysis of the system performance in different time steps. System resilience is measured as a time-dependent function with respect to the change of system performance. The applicability of the proposed methodology is demonstrated by a case study, and the effects of different configurations of safety barriers on improving resilience are discussed. The results are valuable to support disaster prevention within chemical industrial areas.     

A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors

INTRODUCTION: Focusing on people and organizations, this paper aims to contribute to offshore safety assessment by proposing a methodology to model causal relationships. METHOD: The methodology is proposed in a general sense that it will be capable of accommodating modeling of multiple risk factors considered in offshore operations and will have the ability to deal with different types of data that may come from different resources. Reason's "Swiss cheese" model is used to form a generic offshore safety assessment framework, and Bayesian Network (BN) is tailored to fit into the framework to construct a causal relationship model. The proposed framework uses a five-level-structure model to address latent failures within the causal sequence of events. The five levels include Root causes level, Trigger events level, Incidents level, Accidents level, and Consequences level. To analyze and model a specified offshore installation safety, a BN model was established following the guideline of the proposed five-level framework. A range of events was specified, and the related prior and conditional probabilities regarding the BN model were assigned based on the inherent characteristics of each event. RESULTS: This paper shows that Reason's "Swiss cheese" model and BN can be jointly used in offshore safety assessment. On the one hand, the five-level conceptual model is enhanced by BNs that are capable of providing graphical demonstration of inter-relationships as well as calculating numerical values of occurrence likelihood for each failure event. Bayesian inference mechanism also makes it possible to monitor how a safety situation changes when information flow travel forwards and backwards within the networks. On the other hand, BN modeling relies heavily on experts' personal experiences and is therefore highly domain specific. IMPACT ON INDUSTRY: "Swiss cheese" model is such a theoretic framework that it is based on solid behavioral theory and therefore can be used to provide industry with a roadmap for BN modeling and implications. A case study of the collision risk between a Floating Production, Storage and Offloading (FPSO) unit and authorized vessels caused by human and organizational factors (HOFs) during operations is used to illustrate an industrial application of the proposed methodology.     

Prediction of occupational accident statistics and work time loss distributions using Bayesian analysis

This paper uses Bayesian analysis tools for the stochastic evaluation of work time losses due to occupational accidents in a workplace. Models are developed for accident frequencies, duration of recovery from an accident, and the worker unavailability. The unavailability statistics are hereby derived considering a two state stochastic model, to provide estimates for the expected work time losses over a base period of workplace operation. The above models are applied on real multiyear accident data collected from the Greek Petrochemical Industry.     

Reliability evaluation of fire alarm systems using dynamic Bayesian networks and fuzzy fault tree analysis

In almost all industries, fire alarm systems play a vital role in the reducing the risks associated with fires and damages. Therefore, it is necessary to evaluate their reliability and performance in emergency situations. The present study aimed to use fault tree analysis (FTA) to determine the root causes involved in the failure of fire alarm systems, to use Fuzzy set theory and expert elicitation to determine relative probabilities, and finally, to evaluate the reliability of a fire alarm system using dynamic Bayesian networks (BNs) during a thirty-six months period. A total of 29 basic events were detected from the FT. The reliability of the fire alarm system was estimated at 0.954 according to the FT and 0.957 according to conventional BNs. The reliability of the fire alarm system after 36 months was estimated at 0.375 according to dynamic BNs. All the events involved in the failure of fire alarm systems were drawn in the fault tree diagram. The results indicate that remodeling of these systems and simultaneous construction activities are the most important factors in the failure of the fire alarm system. System reliability can also be increased to 0.965 by providing preventive and control measures to reduce the probability of critical events.     

Work role centrality of industrial workers as related to organizational conditions,task autonomy,managerial orientations and personal characteristics

The study shows that, for a sample of 818 industrial workers, work role centrality (WRC) is positively related to task autonomy and to organizational conditions of sector of ownership, unit-type of production system, and production stability; it is also weakly related to the orientations of production managers concerning the sharing of information, general supervision and granting autonomy. The task autonomy - work role centrality relationship is maintained beyond other organizational conditions, but the effects of managerial orientations are attenuated by task autonomy and by organizational conditions. Situational conditions such as production system and stability, managerial orientations and task autonomy add to the variance of WRC explained by personal attributes of gender, education and job training, and by the outcome of job satisfaction, and a combined model of both types of variables is the most efficient. Generally, work role centrality is found to be a fairly sensitive response to the work situation, but it is suggested that perceptual and attitudinal data be added to the explanatory model.     

Not all is fair in love and work: coworkers' preferences for and responses to managerial interventions regarding workplace romances

This article proposes a model of coworkers' preferences for and responses to managerial interventions regarding workplace romances (WRs). Specifically, the model examines the antecedents of coworkers' preferences for managerial intervention in WRs and the consequences of whether their preferred intervention is congruent with the actual managerial intervention. According to the model, coworkers' preferences for managerial intervention are influenced by their perceptions of the impact of the WR on the work environment, including perceptions of whether it represents a conflict of interest for the two participants and whether it disrupts the functioning of the work group. These perceptions are affected by factors pertaining to the romance itself, the coworkers themselves, and the organizational context in which the romance occurs. Next, consistent with the organizational justice literature, we propose that coworkers will evaluate the distributive and procedural justice of the managerial intervention according to (a) the congruence between the severity of their preferred managerial action and the severity of the actual managerial action and (b) the congruence between their beliefs about a just process for managerial decision making about WRs and the actual process. Finally, we propose that coworkers who perceive the managerial intervention process and outcomes as unjust will exhibit negative work‐related attitudes (e.g., decreased job satisfaction and organizational commitment) and behavior (e.g., decreased productivity and organizational citizenship behaviors). Implications of the model for future research and organizational practice are discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

Risk analysis of deepwater drilling operations using Bayesian network

Deepwater drilling is one of the high-risk operations in the oil and gas sector due to large uncertainties and extreme operating conditions. In the last few decades Managed Pressure Drilling Operations (MPD) and Underbalanced Drilling (UBD) have become increasingly used as alternatives to conventional drilling operations such as Overbalanced Drilling (OVD) technology. These newer techniques provide several advantages however the blowout risk during these operations is still not fully understood. Blowout is regarded as one of the most catastrophic events in offshore drilling operations; therefore implementation and maintenance of safety measures is essential to maintain risk below the acceptance criteria. This study is aimed at applying the Bayesian Network (BN) to conduct a dynamic safety analysis of deepwater MPD and UBD operations. It investigates different risk factors associated with MPD and UBD technologies, which could lead to a blowout accident. Blowout accident scenarios are investigated and the BNs are developed for MPD and UBD technologies in order to predict the probability of blowout occurrence. The main objective of this paper is to understand MPD and UBD technologies, to identify hazardous events during MPD and UBD operations, to perform failure analysis (modelling) of blowout events and to evaluate plus compare risk. Importance factor analysis in drilling operations is performed to assess contribution of each root cause to the potential accident; the results show that UBD has a higher occurrence probability of kick and blowout compared to MPD technology. The Rotating Control Devices (RCD) failure in MPD technology and increase in flow-through annulus in UBD technology are the most critical situations for kick and blowout.     

Organizational and behavioural consequences of uncertainty: A case study

This paper examines some of the organizational and behavioural consequences of uncertainty through consideration of a case study. An industrial firm and its problems are described and an attempt is made to understand the origins of the situation using a systems analysis which draws on constructs from the structural, the behavioural and the ‘bureaucratic’ traditions within organization theory. The analysis demonstrates: that the problems are interrelated and stem from the structural arrangements in the firm which are unable to cope with the prevailing uncertainties; that the behavioural difficulties may be seen as manifestations of the underlying structural/environmental mismatch; and, that the problems are exacerbated by apparently logical but paradoxically inappropriate managerial choices and responses. The implications of this study for theory, practice and method are discussed.     

On the contribution to the alignment during an organizational change: Measurement of job satisfaction with working conditions

Introduction: Modern approaches to Occupational Health and Safety have acknowledged the important contribution that continuous improvements to working conditions can make to the motivation of employees, their subsequent performance, and therefore to the competitiveness of the company. Despite this fact, organizational change initiatives represent a path less traveled by employees. Specialized literature has drawn on the fact that employees’ satisfaction presents both the foundation and catalyst for effective implementation of improvements to working conditions. Method: This paper conceptualizes the alignment of employees through measurement of job satisfaction and uses the Bayesian Network to assess the influence of human factors, particularly the cognitive, emotional, and behavioral aspects. Toward this aim, the Bayesian Network is evaluated through a cross-validation process, and a sensitivity analysis is then conducted for each influential dimension: emotional, cognitive, and behavioral. Results: The results reveal that these three dimensions are interrelated and have a direct influence on job satisfaction and employees’ alignment during the organization change. Further, they suggest that the best strategy for enhanced alignment and smooth conduct of organizational changes is simultaneous enhancement of the three dimensions. Practical applications: This study shows the influence of emotional, cognitive, and behavioral dimensions on job satisfaction and employees’ alignment during the organizational change. Furthermore, it elaborates the way to develop efficient and effective strategies for a successful change implementation and sustained alignment.     

A bayesian network-based safety assessment method for solid propellant granule-casting molding process

The safety of the solid propellant molding process is vital for the stable production of high-quality propellants. Failure events caused by abnormal parameters in the molding process may have catastrophic consequences. In this paper, a Bayesian network (BN) model is proposed to assess the safety of the solid propellant granule-casting molding process. Fault tree analysis (FTA) is developed to construct a causal link between process variables and process failures. Subsequently, expert experience and fuzzy set theory (FST) are used to obtain failure probabilities of the basic events (BEs). Based on the mapping rules, FTA provides BN with reliable prior knowledge and a network structure with interpretability. Finally, when new evidence is obtained, the probability is updated with the diagnostic reasoning capability of BN. The results of the sensitivity analysis and diagnostic inference were combined to identify key parameters in the granule-casting molding process, including curing temperature, vacuum degree, extrusion, calendering roll distance, length setting value, holding time, and polish time. The results of this paper can provide effective supporting information for managers to conduct process safety analysis.     

基于贝叶斯网络的煤矿事故人因推理

煤矿事故的不可重现性决定了事故原因的调查具有很强的不确定性,如何通过事故发生后的相关信息提高事故深层次原因调查的准确性是非常重要的。将HFACS与贝叶斯网络相结合,以煤矿事故HFACS分析结果为样本,通过卡方检验和让步比分析建立人因的贝叶斯网络因果图,进一步利用最大似然估计算法确定了煤矿事故人因的贝叶斯网络参数。最后,以双柳煤业顶板事故的调查信息为证据推理导致煤矿事故发生的深层次原因,提高事故原因调查的准确性,从而验证模型的有效性。     

Dynamic risk analysis of offloading process in floating liquefied natural gas (FLNG) platform using Bayesian Network

The growing demand for natural gas has pushed oil and gas exploration to more isolated and previously untapped regions around the world where construction of LNG processing plants is not always a viable option. The development of FLNG will allow floating plants to be positioned in remote offshore areas and subsequently produce, liquefy, store and offload LNG in the one position. The offloading process from an FLNG platform to a gas tanker can be a high risk operation. It consists of LNG being transferred, in hostile environments, through loading arms or flexible cryogenic hoses into a carrier which then transports the LNG to onshore facilities. During the carrier's offloading process at onshore terminals, it again involves risk that may result in an accident such as collision, leakage and/or grounding. It is therefore critical to assess and monitor all risks associated with the offloading operation. This study is aimed at developing a novel methodology using Bayesian Network (BN) to conduct the dynamic safety analysis for the offloading process of an LNG carrier. It investigates different risk factors associated with LNG offloading procedures in order to predict the probability of undesirable accidents. Dynamic failure assessment using Bayesian theory can estimate the likelihood of the occurrence of an event. It can also estimate the failure probability of the safety system and thereby develop a dynamic failure assessment tool for the offloading process at a particular FLNG plant. The main objectives of this paper are: to understand the LNG offloading process, to identify hazardous events during offloading operation, and to perform failure analysis (modelling) of critical accidents and/or events. Most importantly, it is to evaluate and compare risks. A sensitivity analysis has been performed to validate the risk models and to study the behaviour of the most influential factors. The results have indicated that collision is the most probable accident to occur during the offloading process of an LNG carrier at berth, which may have catastrophic consequences.     

The relationship between managerial motivation,leadership, nurse outcomes and patient satisfaction

This ex‐post facto/correlational study examines the relationships among the following variables: nurse manager motivation for power, achievement and affiliation (N=19), managerial leadership behaviors, staff nurse outcomes of job satisfaction, productivity and organizational commitment (N=221) and patient satisfaction (N=299). Results show that managerial motivation for power is negatively correlated with manager use of leadership behaviors and staff nurse job satisfaction but positively correlated with patient satisfaction. Managerial motivation for achievement is positively correlated with use of leadership behaviors as well as nurse job satisfaction, productivity and organizational commitment, and generally to patient satisfaction. Managerial motivation for affiliation reveals few significant positive or negative relationships with other variables. The researcher concludes that both power and achievement motivation of the manager influence staff and patient outcomes in health care in the 1990s. Copyright © 1999 John Wiley & Sons, Ltd.     

Bayesian estimation and consequence modelling of deliberately induced domino effects in process facilities

Process facilities handling hazardous chemicals in large quantities and elevated operating conditions of temperature/pressure are attractive targets to external attacks. The possibility of an external attack on a critical installation, performed with an intention of triggering escalation of primary incidents into secondary and tertiary incidents, thereby increasing the severity of consequences needs to be effectively analysed. A prominent Petrochemical Industry located in Kerala, India was identified for studying the possibility of a deliberately induced domino effect. In this study, a dedicated Bayesian network is developed to model the domino propagation sequence in the chemical storage area of the industry, and to estimate the domino probabilities at different levels. This method has the advantage of accurately quantifying domino occurrence probabilities and identifying possible higher levels of escalations. Moreover, the combined effect from multiple units can be modelled easily and new information can be added into the model as evidences to update the probabilities. Phast (Process hazard analysis) software is used for consequence modelling to determine the impact zones of the identified primary and secondary incidents. The results of the case study show that such analyses can greatly benefit green field and brown field projects in determining the appropriate safety and security measures to be implemented or strengthened so as to reduce its attractiveness to external threat agents.     

Probabilistic analysis of aluminium production explosion accidents based on a fuzzy Bayesian network

Explosion accidents of molten aluminium in contact with water during aluminium production often occur and may cause injury and death. In this paper, a fuzzy Bayesian network (BN) was employed to probabilistically analyse the explosion accident of molten aluminium in contact with water. A fault tree-Bayesian network (FT-BN) model was established in the cause-effect analysis of the explosion accident, including three processes: electrolysis, molten aluminium transportation and aluminium casting. Fifty-three nodes were proposed in the model to represent the evolution process of the explosion accident from failure causes to consequences. Furthermore, the occurrence probabilities of basic events (BEs) were determined by expert judgement with weighted treatments based on fuzzy theory. By giving certain occurrence probabilities of each BE, the probability of an explosion accident was estimated. Subsequently, importance measures were assessed for each BE, which could reflect the impact on the occurrence of the top event (TE), and the final ranks were provided. The results indicate that using wet ladles and tools, water on the ground, breakage of the tap hole, damage to the casting mould, and leakage of circulating water are five main problems that cause explosion accidents. Safety advice was provided based on the analysis results. This study can help decision makers improve the safety management of aluminium production.     

Religion and attitudes toward work: A new look at an old question

This study examined the relationship of specific religions and degree of religious beliefs to various job-related attitudes and values such as motivational needs, job satisfaction, work ethic, and work, job and organizational commitment. Subjects were 222(107 male, 115 female) managerial and non-managerial workers in the Rocky Mountain area. Results showed no significant correlations between work-related altitudes and specific religious affiliation or degree of religious conviction. However, religious conviction was significantly finked to organizational rank, with non-managerial employees reporting a higher degree of religious conviction than managerial employees. Protestant workers scored higher in Protestant Work Ethic than did non-Protestants and persons with no religious affiliation had higher need for Power than Protestants, Catholics or affiliates of Eastern religions.