充填管道系统失效模式与影响分析(FMEA)及失效影响模糊评估

根据金川矿区充填管道系统的运行情况,结合充填实践经验,建立了充填管道系统的失效模式与影响分析法(FMEA)分析表,得出系统存在10种失效模式。针对FMEA分析表中失效影响存在较大模糊性而难以有效估计的特点,引入模糊评估方法来进行分析。通过该矿区充填管道系统各失效模式产生失效影响的模糊评估,得到不同失效模式对系统可靠性影响的排序结果。结果表明:该方法的评估结果与实际情况比较吻合;可以使分析定量化,有助于工程人员充分利用系统模糊信息;该方法也能用于其他系统失效模式影响的模糊评估。     

System failure probability evaluation using fault tree analysis and expert opinions in intuitionistic fuzzy environment

In quantitative fault tree analysis of a system, exact failure probability values of components are utilized to calculate the failure probability of the system. However, in many real world problems, it is problematic to get precise and sufficient failure data of system components due to insufficient or imprecise information about components, changing environment or new components. A methodology has already been developed by employing fuzzy set theory for the system reliability evaluation by utilizing qualitative failure data of system components when quantitative failure data of components are inaccessible or insufficient. This paper extends the concept of fuzzy set to intuitionistic fuzzy set and proposes a novel approach to evaluate system failure probability using intuitionistic fuzzy fault tree analysis with qualitative failure data of system components. The qualitative failure data such as expert opinions are collected as linguistic terms. These linguistic terms are then quantified by triangular intuitionistic fuzzy numbers in form of membership function and non-membership function. Additionally, a method is developed for combining the different opinions of experts. To illustrate the applicability of proposed approach, a case study of the crude oil tank fire and explosion accident is performed. The obtained results are very close to the results from pre-existing approaches which confirm that the proposed approach is a more realistic alternative for the study of system reliability in intuitionistic fuzzy environment when quantitative failure data of system components are not known. To help decision makers for improving the security execution of the crude oil tank system, importance measures including Fussell-Vesely importance and cut sets importance are also executed.     

Risk assessment of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree

The oxygen-enhanced combustor has the advantages of high burning efficiency and low emissions. However, it should not be promoted for industrial use until its reliability and safety have been fully recognized. A new methodology is proposed to assess the risk of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree. In addition, it is applied to a selected pilot semi-industrial combustor. To identify the hazard source comprehensively, the pilot is divided into four subsystems: the combustor subsystem, feed subsystem, ignition subsystem and exhaust subsystem. According to the operational parameters of flow (flow rate, temperature and pressure) and the component functions in different subsystems, the cause and effect matrix can be built using the structural model, and the relationship between the operational parameters and the effects of the change for the operational parameters on the system can be presented. Based on the results of cause and effect matrix, the FMEA can be built to describe the failed models and accident scenarios of the pilot. The main accident forms include leakage, injury, fire and explosion. Accordingly, with the severity and probability analysis of different accident forms, the fire and explosion accidents should be further accessed quantitatively using the fuzzy fault tree analysis. The fault trees can be obtained in accordance with the FMEA, and the qualitative assessments of the basic events can be collected by using expert scoring. A hybrid approach for the fuzzy set theory and weight analysis is investigated to quantify the occurrence probability of basic events. Then, the importance analysis of the fault trees, including the hazard importance of basic events and the cut set importance, is performed to help determine the weak links of the fire and explosion trees. Finally, some of the most effective measures are presented to improve the reliability and safety of the combustion system.     

Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry

In this research, a framework combining lean manufacturing principles and fuzzy bow-tie analyses is used to assess process risks in chemical industry. Lean manufacturing tools and techniques are widely used for eliminating wastes in manufacturing environments. The five principles of lean (identify value, map the value stream, create flow, establish pull, and seek perfection) are utilized in the risk assessment process. Lean tools such as Fishbone Diagram, and Failure Mode and Effect Analysis (FMEA) are used for risk analysis and mitigation. Lean principles and tools are combined with bow-tie analysis for effective risk assessment process. The uncertainty inherent with the risks is handled using fuzzy logic principles. A case study from a chemical process industry is provided. Main risks and risk factors are identified and analyzed by the risk management team. Fuzzy estimates are obtained for the risk factors and bow-tie analysis is used to calculate the aggregated risk probability and impact. The risks are prioritized using risk priority matrix and mitigation strategies are selected based on FMEA. Results showed that the proposed framework can effectively improve the risk management process in the chemical industry.     

Failure Mode and Effect Analysis using an integrated approach of clustering and MCDM under pythagorean fuzzy environment

Failure Mode and Effect Analysis (FMEA) is an effective risk analysis and failure avoidance approach in the design, process, services, and system. With all its benefits, FMEA has three limitations: failure mode risk assessment and prioritization, complex FMEA worksheets, and difficult application of FMEA tables. This paper seeks to overcome the shortcomings of FMEA using an integrated approach based on a developed Pythagorean fuzzy (PF) k-means clustering algorithm and a popular MCDM method called PF-VIKOR. In the first step, Pythagorean fuzzy numbers (PFNs) were used to collect Severity (S), Occurrence (O), and Detection (D) factors for failure modes to incorporate uncertainty and fuzziness into subjective judgments. Afterward, failure modes were clustered by developing a novel k-means clustering algorithm that accepts PFNs as input. Finally, the PF-VIKOR approach was used to analyze the ordering of cluster risks. The proposed approach was implemented in the dehydration unit of an Iranian gas refinery and the results were compared with the traditional FMEA. The findings showed the flexibility and applicability of the proposed approach in addressing real-world problems. This research provides two key contributions: (1) designing a PFN-based k-means clustering algorithm that tackles FMEA limitations and (2) using the PF-VIKOR method for prioritizing and evaluating failure mode clusters.     

基于云模型的电梯运行可靠性模糊综合评估

针对目前电梯可靠性评估中存在随机性和模糊性等不确定性的问题,提出了一种基于熵权法、云模型和模糊综合评价法的电梯运行可靠性评估模型。通过分析电梯各个子系统的主要特征量,建立了电梯运行可靠性评估指标体系,并运用熵权法确定各指标的权重。根据云模型实现定量指标与定性概念之间的转换,得到定量评估指标的等级隶属度。最后基于模糊综合评价法,对各指标信息进行自上而下地综合,得到整体的可靠性评估结果。实例表明了该模型的正确性与有效性,能够对电梯的状态检修提供参考。     

Qualitative risk assessment of a Dual Fuel (LNG-Diesel) system for heavy-duty trucks

A Dual Fuel (LNG-Diesel) system can be applied to heavy-duty diesel trucks for achieving environmental benefits in comparison to existing diesel vehicles. Because of lack of reports about risk assessment of this technology, we performed a qualitative assessment based on a framework of some literature techniques for risk identification, analysis and evaluation. After constructing a Reliability Block Diagram (RBD) to establish the context, we conducted bow-tie analysis, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), likelihood and consequence analysis, and used a risk matrix. We applied these methods and techniques qualitatively to identify causes (e.g. collisions), critical events (e.g. releases of natural gas), related consequences (e.g. fires and explosions), and different possible pathways from a specific cause to its consequence, and to assess some negative accident scenarios related to use and parking of the vehicle. The bow-tie analysis also allowed to make explicit barriers and controls that prevent critical events and/or mitigate consequences. Therefore, we identified a set of safety measures, including design, technical, management, and emergency actions, which shall be implemented in each step of the system's life cycle.Our risk assessment showed that the risk level of the Dual Fuel (LNG-Diesel) system is similar to the risk level of a traditional diesel system. Future research will overcome current lack of data and, therefore, permit a quantitative rating of the risk of the Dual Fuel (LNG-Diesel) system.     

信息资源共享系统可靠性模型的研究

信息资源共享系统可以充分利用信息的传递性、共享性,支持异地协同工作,使人们获取信息更为经济、便利、迅速。信息资源共享系统有其自身的脆弱性,系统安全可靠是最重要、最基本的需求。提高系统可靠性,预防不可靠因素是信息资源共享系统设计的关键之一。笔者提出了信息资源共享系统可靠性的概念。由信息资源共享系统可靠性理论导出信息资源共享系统可靠度,建立了信息资源共享系统可靠性优化的多目标数学规划模型。为信息资源共享系统可靠性建立了定量分析方法。最后以可靠度、成本和时间3个优化目标为例,进行了计算、分析。为信息资源共享系统可靠性设计提供了新方法。     

大空间建筑消防系统故障风险定量分析的新方法

在对CO2自动灭火系统的故障模式、影响及危害度定性分析的基础上,采用模糊数学方法,以系统故障影响严重度、故障模式发生概率及其可检测性为关键指标,给出了故障风险定量评价指标的建立方法,提出了一种消防系统故障风险定量分析的新方法———模糊推理法。该方法可直接输入语言变量,利用模糊定性和不确定信息或数据定量评价系统的故障风险,并以CO2自动灭火系统为例,详细阐述了该方法定量评价故障风险的全过程。该方法和推理过程可为其他建筑防火设备的同类分析所借鉴,为城市火灾风险定量评价系统的构建提供理论基础和一种新方法。     

基于模糊推理的FRAM事故定量分析方法研究

为有效克服FRAM事故分析中无法进行定量分析的缺陷，提出结合模糊推理技术的Fuzzy FRAM模型。此改进模型基于FRAM识别系统运行状态；依据功能输出要素的时间/精度属性利用Matlab构建2阶模糊推理系统量化功能输出质量；根据通用性能条件(CPC)及功能输入耦合端口构建功能评价体系，针对评价体系中存在的不确定性信息融合及建模问题，采用模糊证据推理技术，通过模糊信度结构建立、数据处理、信息融合测度后获得功能的风险指数;以既有铁路危险品运输事故为例，验证方法的可行性。结果表明:Fuzzy FRAM模型的评估结果较为精确，是FRAM分析方法的有效补充。     

矿山企业安全管理交互式模糊综合评价决策支持系统设计

安全管理是企业管理的重要组成部分，是实现安全生产的有力保证。决策支持系统是实现安全管理定性与定量分析有机结台的有效手段。从建立实用的安全管理信息系统的角度出发，详细论述了系统分析与系统设计的思想、方法、内容和步骤，阐述了开发矿山企业安全管理交互式模糊综合评价决策支持系统（SMDSS）的意义，介绍了SMDSS的基本结构和基本功能，描述了SMDSS的综合评价机理，并给出了SMDSS的人机交互式评价过程。     

Developing fuzzy signal detection theory for workers’ hazard perception measures on subway operations

The ability to perceive hazards has been suggested as being an important factor in determining safety risk. By measuring the ability to perceive hazard, at-risk workers could be identified and provided with additional training. This can reduce knowledge-based human errors and thereby help to avoid accidents. This paper proposes the use of fuzzy signal detection theory (FSDT), which combines fuzzy logic and conventional signal detection theory (SDT) to measure hazard perception. The flexibility of the proposed method allows multiple forms of qualitative and quantitative information to be used, including expert opinions and workers’ responses when quantitative data are lacking or when only qualitative aspects are known. Details have been provided on the basic postulates, formulas, and steps involved in FSDT analysis. A real-world example demonstrates the feasibility of the proposed framework. The results of a case study show that the participating workers had a medium level of hazard perception. This means that these workers were sensitive to the difference between hazard and safety. But there were still opportunities for improvement. Of the workers surveyed, five workers expressed conservative views regarding risk while the other workers expressed risky views. FSDT increases the applicability of conventional SDT analysis to many real-world settings, where the definition of a signal is less dichotomous than in a controlled laboratory environment.     

Application of fuzzy logic to explosion risk assessment

Safety and health of workers potentially being at risk from explosive atmospheres are regulated by separate regulations (ANSI/AIHA in USA and ATEX in the European Union). The ANSI/AIHA does not require risk assessment whereas it is compulsory for ATEX. There is no standard method to do that assessment. For that purpose we have applied the explosion Layer of Protection Analysis (ExLOPA), which enables semi-quantitative risk assessment for process plants where explosive atmospheres occur. The ExLOPA is based on the original work of CCPS for LOPA taking into account an explosion accident scenario at workplace. That includes typical variables appropriate for workplace explosion like occurrence of the explosive atmosphere, the presence of effective ignition sources, activity of the explosion prevention and mitigation independent protection layers as well as the severity of consequences. All those variables are expressed in the form of qualitative linguistic categories and relations between them are presented using expert based engineering knowledge, expressed in the form of appropriate set of rules. In this way the category of explosion risk may be estimated by the semi-quantitative analysis. However, this simplified method is connected with essential uncertainties providing over or under estimation of the explosion risk and may not provide real output data.In order to overcome this problem and receive more detailed quantitative results, the fuzzy logic system was applied. In the first stage called fuzzification, all linguistic categories of the variables are mapped by fuzzy sets. In the second stage, the number of relation between all variables of analysis are determined by the enumerative combinatorics and the set of the 810 fuzzy rules “IF-THEN” is received. Each rule enables determination of the fuzzy risk level for a particular accident scenario. In the last stage, called defuzzification, the crisp value of final risk is obtained using a centroid method. The final result of the risk presents a contribution of each risk category represented by the fuzzy sets (A, TA, TNA and NA) and is therefore more precise and readable than the traditional approach producing one category of risk only. Fuzzy logic gives a possibility of better insights into hazards and safety phenomena for each explosion risk scenario. It is not possible to receive such conclusions from the traditional ExLOPA calculation results. However it requires the application of computer-aided analyses which may be partially in conflict with a simplicity of ExLOPA.The practical example provides a comparison between the traditional results obtained by ExLOPA and by fuzzy ExLOPA methods.     

A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis

IntroductionWith the development of industries and increased diversity of their associated hazards, the importance of identifying these hazards and controlling the Occupational Health and Safety (OHS) risks has also dramatically augmented. Currently, there is a serious need for a risk management system to identify and prioritize risks with the aim of providing corrective/preventive measures to minimize the negative consequences of OHS risks. In fact, this system can help the protection of employees’ health and reduction of organizational costs. Method: The present study proposes a hybrid decision-making approach based on the Failure Mode and Effect Analysis (FMEA), Fuzzy Cognitive Map (FCM), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for assessing and prioritizing OHS risks. After identifying the risks and determining the values of the risk assessment criteria via the FMEA technique, the attempt is made to determine the weights of criteria based on their causal relationships through FCM and the hybrid learning algorithm. Then, the risk prioritization is carried out using the MOORA method based on the decision matrix (the output of the FMEA) and the weights of the criteria (the output of the FCM). Results: The results from the implementation of the proposed approach in a manufacturing company reveal that the score at issue can overcome some of the drawbacks of the traditional Risk Priority Number (RPN) in the conventional FMEA, including lack of assignment the different relative importance to the assessment criteria, inability to take into account other important management criteria, lack of consideration of causal relationships among criteria, and high dependence of the prioritization on the experts’ opinions, which finally provides a full and distinct risk prioritization.     

A novel Dempster-Shafer theory-based approach with weighted average for failure mode and effects analysis under uncertainty

Risk priority number (RPN) is a commonly-used prioritization method in failure mode and effects analysis (FMEA) for systemic reliability and safety study. However, conventional RPN confronts wide criticisms, due to the neglect of the uncertainties of experts' opinions. It is important to handle the conflicts among experts' multi-opinions which could be described in imprecise, incomplete or crisp forms in lack of knowledge. In this paper, a novel method is proposed to alleviate and/or eliminate counter-intuitive behaviors against conflicts among multi-opinions under Dempster-Shafer theory. Firstly, the abnormity test for experts’ opinions is undertaken to identify and discard those distinct judgments. Then, an approach to determining the subjective weights of experts is proposed, which is used in weighted average for multi-opinions before calculating RPNs. Finally, the risk priority evaluation on main engine crankcase explosion failure on-board ship is implemented to verify the feasibility of the proposed approach.     

An extension of the fuzzy improved risk graph and fuzzy analytical hierarchy process for determination of chemical complex safety integrity levels

The risk graph (RG) is widely used to evaluate the safety integrity level (SIL) of safety instrument systems (SIS). However, subjective opinion-based conventional RGs cannot provide successful results for the problems of risk parameters, such as shortages or lack of data; hence, the output of a conventional approach lacks sufficient reliability. We introduced the fuzzy improved risk graph (FIRG), an extension of fuzzy set theory, to deal with possible ambiguities during SIL study and increase the reliability of conventional RGs. In the present study, the levels of consequences defined as linguistic terms were converted into qualitative intervals; therefore, by correlating the proposed approach with experts’ opinions and attributing weight factors, a desired SIL value was obtained. The output of this new approach can be compared directly with quantitative risk assessment techniques to improve the safety performance of industrial systems.     

Human error analysis in furnace start-up operation using HEART under intuitionistic fuzzy environment

To address human error in system reliability, Human Reliability Analysis (HRA) is an essential issue. Human Error Assessment and Reduction Technique (HEART) as a rather straightforward technique for HRA has successfully been used in many areas to predict human error probability (HEP). However, knowledge acquisition of experts during assessed proportion of affect (APOA) calculation is subjected to vagueness and ambiguity. To overcome this challenge, in this paper Intuitionistic Fuzzy (IF) set due to their advantage to represent more fuzzy information than a classical fuzzy set adopted through APOA calculation. To demonstrate this hybrid approach short for, IF-HEART, the furnace start-up operation is handled, since analysis shows that most of explosions and losses occur during furnace start-ups operation. Further, a sensitivity analysis is carried out to approve the proposed integrated approach. In addition to its academic contribution, the results of the paper enable to improve the overall safety level of a furnace by taking into account potential human error.     

工业企业电气安全评价方法研究

针对现行电气安全评价方法存在的不足,提出了工业企业电气安全模糊综合评价方法.依据事故理论的综合论观点,建立了多层次电气安全评价因素体系;采用定性与定量相结合的层次分析法(AHP)确定评价因素的权重;采用隶属频率统计法确定单因素隶属度;最后应用多层次模糊综合评判模型合成评价结果,并使用安全等级特征量对模糊综合评价结果进行处理,得出工业企业电气系统的安全等级.结果表明,将模糊综合评价方法引入工业企业电气安全现状评价具有一定的现实意义,克服了现行评价方法不够全面或不够定量的缺点;安全等级模糊特征量是处理模糊综合评价结果更优化的方法;该评价方法紧密结合企业现状,可在一般工业企业的电气安全评价中广泛应用,其配套评价软件更为企业安全管理部门和安全评价机构提供了便捷的应用工具.