A new risk prioritization model for failure mode and effects analysis

Failure modes and effects analysis is a framework that has been widely used to improve reliability by prioritizing failures modes using the so‐called risk priority number. However, the risk priority number has some problems frequently pointed out in literature, namely its non‐injectivity, non‐surjectivity, and the impossibility to give weights to risk variables. Despite these disadvantages, the risk priority number continues to be widely used due to its higher simplicity when compared with other alternatives found in literature. In this paper, we propose a novel risk prioritization model to overcome the major drawbacks of the risk priority number. The model contains 2 functions, the risk isosurface function that prioritizes 3 risk variables considering their order of importance in a given risk scenario, and the risk prioritization index function which prioritizes 3 risk variables considering their weights. The novelty of the proposed model is its injectivity, surjectivity, and ease of use in failure modes prioritization. The performance of the proposed model was analyzed using some examples typically used to discuss the conventional risk priority number shortcomings. The model was applied to a case study and its performance correlated with other risk prioritization models. Results show that the failure modes prioritization reached with the proposed model agrees with the expectations made for the risk scenario.     

An interval probability-based FMEA model for risk assessment: A real-world case

Failure mode and effect analysis (FMEA) is an effective quality tool to eliminate the risks and enhance the stability and safety in the fields of manufacturing and service industry. Nevertheless, the conventional FMEA has been criticized for its drawbacks in the evaluation process of risk factors or the determination of risk priority number (RPN), which may lead to inaccurate evaluation results. Therefore, in this paper, we develop a novel FMEA method based on rough set and interval probability theories. The rough set theory is adopted to manipulate the subjectivity and uncertainty of experts' assessment and convert the evaluation values of risk factors into interval numbers. Meanwhile, the interval exponential RPN (ERPN) is used to replace the traditional RPN due to its superior properties, eg, solving the problems of duplicate numbers and discontinuity of RPN values. Furthermore, an interval probability comparison method is proposed to rank the risk priority of each failure mode for avoiding the information loss in the calculation process of RPN. Finally, a real case study is presented, and the comparison analysis among different FMEA methods is conducted to demonstrate the reliability and effectiveness of the proposed FMEA method.     

A developed autonomous preventive maintenance programme using RCA and FMEA

Total productive maintenance (TPM) was developed in Japan in 1971 and has since been phased into many manufacturing firms to promote productivity and competitiveness. Autonomous preventive maintenance (APM) systems are very special. The fundamental pillar of TPM includes a series of important systematical first-line direct labour activities. The technical cost, human resources and management issues are all considered. Failure modes and effects analysis (FMEA) and root-cause analysis (RCA) are the most popular failure analytical methods, widely adopted over different industries. They are often used to examine the potential problems in the design and manufacturing phase, discovering possible failure causes before product design and manufacturing finalisation. This study integrates the RCA and FMEA techniques to establish an APM system that meets a company’s goal of reducing manufacturing costs and promoting employee and equipment productivity. The major contribution of this study is constructing potential equipment failure modes and their risk priority number through RCA and FMEA integration transformed into a selection of items and their APM maintenance frequencies. A strategy for deploying employee technical capability upgrade through effective training is developed. This study uses the S Company – a key manufacturer of semiconductor material – as a case study to verify the model’s applicability and suitability.     

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Failure modes and effects analysis (FMEA) is a safety and reliability technique that is widely used to evaluate, design, and process a system against diverse possible ways through which the potential failure has a tendency to occur. In conventional FMEA, the risk evaluation is determined by risk priority number (RPN) obtained by multiplying of three risk factors—severity, occurrence, and detection. However, because of many shortages in conventional FMEA, the RPN scores have been widely criticized along issues bothering on ambiguity and vagueness, scoring, appraising, evaluating, and selecting corrective actions. In this paper, we propose a new integrated fuzzy smart FMEA framework where the combination of fuzzy set theory, analytical hierarchy process (AHP), and data envelopment analysis (DEA) is used, respectively, to handle uncertainty and to increase the reliability of the risk assessment. These are achieved by employing a heterogeneous group of experts and determining the efficiency of FMEA mode with adequate priority and corrective actions using RPN, time, and cost as indicators. A numerical example (aircraft landing system) is provided to exemplify the feasibility and effectiveness of the proposed model. The outputs of the proposed model compared with the conventional risk assessment technique results show its effectiveness, reliability, and propensity for real applications.     

A combined fuzzy DEMATEL and cloud model approach for risk assessment in process industries to improve system reliability

Decision-making trial and evaluation laboratory (DEMATEL) analysis is an effective and comprehensive method for identifying accident factors and converting the relationships among them into a visual structural model. Traditionally, the mean value method is adopted to summarize the initial direct-relation matrix, but it ignores the errors caused by differences in expert knowledge. In addition, a single qualitative risk assessment may not be sufficiently comprehensive and persuasive. The qualitative risk assessment results may not play a complete role in helping industrial plants carry out safety management. Therefore, this study proposes a quantitative risk assessment model based on the cloud model (CM) called the fuzzy DEMATEL-CM. An assessment index model is established by identifying the hazards associated with a converter steelmaking system. Subsequently, fuzzy DEMATEL analysis is applied to determine the relationships among the assessment indices and calculate their weights. Then, the CM is utilized to calculate the risk levels of the assessment indices and determine the comprehensive risk level. Finally, a case study is introduced to verify the practicability and validity of this model, and it is observed that the model has a certain superiority in solving uncertain problems. The quantitative risk assessment results are helpful for preventing accidents to improve the reliability of converter steelmaking plants.     

Introducing a heuristic approach to enhance the reliability of system safety assessment

Probabilistic risk assessment techniques are the important tools which can considerably improve the safety performance of the studied system and reduce the risk to an acceptable level. Typically, decision‐making process is an important part of risk assessment methods that accordingly bring the ambiguity inside. Decision makers as experts commonly express their subjective opinions about the occurrence of the root events in order to obtain the probability of the undesired event. Subsequently, the critical root events are identified, and possible intervention is performed to reduce the probability of the critical events. However, the serious point is the viability of the obtained probabilities and priority ranking of the critical events. In this study, a heuristic optimization model of linear mathematical programming using triangular intuitionistic fuzzy number (TrIFN) is proposed to obtain the feasible, optimum, and reliable results compared with available methods. The Spearman correlation is performed to examine the reliability and behavior of the proposed model. In order to show the effectiveness of the proposed approach, it is applied on a real case study. The application of the model confirms its robustness to prioritize critical root events over the conventional one.     

Applying UGF Concept to Enhance the Assessment Capability of FMEA

The purpose of this paper is to propose a modified version of Failure Mode and Effects Analysis (FMEA) to alleviate its drawbacks. FMEA is an important tool in risk evaluation and finding the priority of potential failure modes for corrective actions. In the proposed method, the Universal Generating Function (UGF) approach has been used to improve the assessment capability of the conventional Risk Priority Number (RPN) in ranking. The new method is named as URPN. It generates the most number of unique values in comparison with the previous methods and considers relative importance for the parameters while it is easy to compute. More unique numbers help to avoid from having the same priority level for different failure modes which represent various risk levels. A case study has been employed to demonstrate that the URPN not only can improve the shortcomings but also is able to provide accurate values for risk assessment. Copyright © 2015 John Wiley & Sons, Ltd.     

A Dynamic Maintenance Planning Framework Based on Fuzzy TOPSIS and FMEA: Application in an International Food Company

A maintenance planning framework is developed in this study to reduce and stabilize the maintenance costs of the manufacturing companies. The framework is based on fuzzy technique for order preference by similarity to ideal solution(TOPSIS) and failure mode and effects analysis (FMEA) techniques and supports maintenance planning decisions in a dynamic way. The proposed framework is general and can easily be adapted to a host of manufacturing environments in a variety of sectors. To determine the maintenance priorities of the machines, fuzzy TOPSIS technique is employed. In this regard, ‘risk priority number’ obtained by FMEA and ‘current technology’, ‘substitutability’, ‘capacity utilization’, and ‘contribution to profit’ are used as the criteria. Performance of the resulting maintenance plan is monitored, and maintenance priorities of the machines are updated by the framework. To confirm the viability of the proposed framework, a real‐world implementation in an international food company is presented. The results of the application reveal that the proposed maintenance planning framework can effectively and efficiently be used in practice. Copyright © 2015 John Wiley & Sons, Ltd.     

ARAMIS project: a more explicit demonstration of risk control through the use of bow-tie diagrams and the evaluation of safety barrier performance

Over the last two decades a growing interest for risk analysis has been noted in the industries. The ARAMIS project has defined a methodology for risk assessment. This methodology has been built to help the industrialist to demonstrate that they have a sufficient risk control on their site.

Risk analysis consists first in the identification of all the major accidents, assuming that safety functions in place are inefficient. This step of identification of the major accidents uses bow–tie diagrams. Secondly, the safety barriers really implemented on the site are taken into account. The barriers are identified on the bow–ties. An evaluation of their performance (response time, efficiency, and level of confidence) is performed to validate that they are relevant for the expected safety function. At last, the evaluation of their probability of failure enables to assess the frequency of occurrence of the accident. The demonstration of the risk control based on a couple gravity/frequency of occurrence is also possible for all the accident scenarios.

During the risk analysis, a practical tool called risk graph is used to assess if the number and the reliability of the safety functions for a given cause are sufficient to reach a good risk control.     

基于多种风险因素的矸石山边坡风险评价

该研究应用可拓理论综合考虑影响矸石边坡稳定的8种风险指标,建立了一套新的矸石边坡稳定性风险评价模型。与传统可拓理论评价模型相比,新模型在可拓距计算方面进行改进,并采用主客观权重相结合的综合分析法对8种风险指标进行权重分析,降低了传统理论算法在边坡稳定性风险评价过程中的系统误差。选取王家岭矸石边坡为案例,经新的边坡稳定性风险评价模型得出,2020年7月7日?2020年8月14日的风险等级变量特征值由原有的1.284上升到2.263,风险等级由低风险上升至中低风险等级,实现了现场矸石山边坡风险的定量评价。通过多个矸石边坡案例应用研究,发现改进的稳定性风险评价模型所得风险等级变量特征可与现有的边坡稳定性评估技术结合使用,实现矿山边坡安全指标与风险指标的综合评价。此外,该方法可以分析现场的实时监测数据,为矿山边坡安全风险评价提供一种客观简便的分析方法,进而有效改变目前矿山边坡工程监测重采集、轻分析的现状。     

Safety climate dimensions as predictors for risk behavior

This study examines the interactive relationship between three dimensions of safety climate (management commitment to safety, priority of safety, and pressure for production), and their impact on risk behavior reported by employees. The sample consisted of 623 employees from a chemical manufacturing organization in South Africa. Hierarchical regression analyses were carried out to test the direct effects and the interaction effect of the three safety climate dimensions on risk behavior. The results showed that, as expected, employees’ risk behavior was negatively related to management commitment to safety and priority of safety and positively related to pressure for production. Moreover, as expected, the three-way interaction between management commitment to safety, priority of safety and pressure for production was significant. When pressure for production was high, management commitment to safety was negatively related to risk behavior, regardless of level of priority of safety on plant. When pressure for production was low, the effect of management commitment to safety on risk behavior was nullified under conditions of high, as compared to low priority of safety on plant. These findings highlight the importance of managerial commitment to safety in contexts where employees experience tensions between production deadlines and safety procedures.     

A comparison of deterministic,reliability-based and risk-based structural optimization under uncertainty

In this paper, the effects of uncertainty and expected costs of failure on optimum structural design are investigated, by comparing three distinct formulations of structural optimization problems. Deterministic Design Optimization (DDO) allows one the find the shape or configuration of a structure that is optimum in terms of mechanics, but the formulation grossly neglects parameter uncertainty and its effects on structural safety. Reliability-based Design Optimization (RBDO) has emerged as an alternative to properly model the safety-under-uncertainty part of the problem. With RBDO, one can ensure that a minimum (and measurable) level of safety is achieved by the optimum structure. However, results are dependent on the failure probabilities used as constraints in the analysis. Risk optimization (RO) increases the scope of the problem by addressing the compromising goals of economy and safety. This is accomplished by quantifying the monetary consequences of failure, as well as the costs associated with construction, operation and maintenance. RO yields the optimum topology and the optimum point of balance between economy and safety. Results are compared for some example problems. The broader RO solution is found first, and optimum results are used as constraints in DDO and RBDO. Results show that even when optimum safety coefficients are used as constraints in DDO, the formulation leads to configurations which respect these design constraints, reduce manufacturing costs but increase total expected costs (including expected costs of failure). When (optimum) system failure probability is used as a constraint in RBDO, this solution also reduces manufacturing costs but by increasing total expected costs. This happens when the costs associated with different failure modes are distinct. Hence, a general equivalence between the formulations cannot be established. Optimum structural design considering expected costs of failure cannot be controlled solely by safety factors nor by failure probability constraints, but will depend on actual structural configuration.     

Optimal use of warning signs in traffic

The aim of the paper is to develop a model of drivers’ behaviour particularly designed to analyse the safety and total driving cost implications of warning sign installations. One special feature of the model is that it makes a clear distinction between drivers’ perceived risk values at certain speeds and their respective objective values. When focusing on a certain stretch of road only, the paper concludes that warning signs will increase safety and probably reduce total objective driving costs; that is the sum of time costs and objective expected accident costs. Since drivers’ speed will reduce implying higher time costs per distance, the reduction in total objective driving costs will be lower than the reductions in accident costs. The analysis is then extended to comprise the whole road system and using warning signs prior to curves as an example. Besides the driving conditions in different curves, the analysis shows that the optimal number of signs is dependent on the road authorities’ objectives for road traffic and on how drivers form their risk perceptions. Generally speaking, simulations indicate that the safety and economic benefits of warning sign installation are not very high. When considering the whole road system, warning signs seem, however, to have a greater positive impact on total driving costs than on accident costs.     

Monte Carlo simulation approach for a modified FMECA in a power plant

This paper presents a new tool for failure mode and effect analysis developed for a new Integrated Gasification and Combined Cycle plant in an important Italian oil refinery. The methodology is based on the integration between a modified Failure Mode Effect and Criticality Analysis (FMECA) and a Monte Carlo simulation as a method for testing the weights assigned to the measure of the risk priority numbers (RPNs). The RPN proposed consists of a weighted sum of six parameters (safety, machine importance for the process, maintenance costs, failure frequency, downtime length, and operating conditions) multiplied by a seventh factor (the machine access difficulty). Adopting this tool and considering the budget constraints, the best maintenance policy has been selected for each plant facility (about 140 in total). Copyright © 2000 John Wiley & Sons, Ltd.     

A methodology for modelling comprehensive international procurement costs

As the economy becomes more globalised and competitive, firms are manufacturing goods in a wider variety of locations. This may be to reduce costs by moving to a low cost country or to place production closer to potential customers. What is often lacking in these facility location decisions is a holistic assessment of the costs associated with production location decisions. Too often the assessment is focused only on a limited set of cost factors (e.g. direct manufacturing and shipping) and does not take into account the dynamic nature of some costs. To address these limitations a comprehensive cost model to assess the cost of procuring goods from alternative locations is presented. A methodology is detailed for monetising many costs associated with international procurement. An illustrative case study analysing the procurement of goods from two locations in Mexico and one in the US is detailed. Results of the case show that the non-direct manufacturing costs associated with procurement (e.g. inventory holding costs and shipping) can be greater than direct manufacturing costs. The effects of fuel and labour cost sensitivity on the alternative locations are also detailed.     

Utility Priority Number Evaluation for FMEA

Traditionally, decisions on how to improve an operation are based on risk priority number (RPN) in the failure mode and effects analysis (FMEA). Many scholars questioned the RPN method and proposed some new methods to improve the decision process, but these methods are only measuring from the risks viewpoint while ignoring the importance of corrective actions. The corrective actions may be interdependent; hence, if the implementation of corrective actions is in proper order, selection may maximize the improvement effect, bring favorable results in the shortest times, and provide the lowest cost. This study aims to evaluate the structure of hierarchy and interdependence of corrective action by interpretive structural model (ISM), then to calculate the weight of a corrective action through the analytic network process (ANP), then to combine the utility of corrective actions and make a decision on improvement priority order of FMEA by utility priority number (UPN). Finally, it verifies the feasibility and effectiveness of this method by application to a case study. An erratum to this article can be found at     

Assessing the effects of inter-vehicle communication systems on road safety

The work described assesses the impact of inter-vehicle communication systems on road safety. A simple model is used to assess the reduction in accident risk if the response time of drivers is improved by state of the art technology. The currently available techniques for inter-vehicle communication, based either on direct communication between vehicles or on vehicle-to-infrastructure and vice versa, are briefly examined in order to provide an overview of the recent proposals the world of electronics and telecommunications has made available to the transport industry concerning safety. A method to assess safety, based on a parameter called risk index (RI), is then proposed in order to evaluate to what extent the use of vehicle-to-vehicle and vehicle-to-infrastructure communication technologies can actually increase road safety.     

Risk, uncertainty and regulation

This paper reviews the relationship between scientific evidence, uncertainty, risk and regulation. Risk has many different meanings. Furthermore, if risk is defined as the likelihood of an event happening multiplied by its impact, subjective perceptions of risk often diverge from the objective assessment. Scientific evidence may be ambiguous. Scientific experts are called upon to assess risks, but there is often uncertainty in their assessment, or disagreement about the magnitude of the risk. The translation of risk assessments into policy is a political judgement that includes consideration of the acceptability of the risk and the costs and benefits of legislation to reduce the risk. These general points are illustrated with reference to three examples: regulation of risk from pesticides, control of bovine tuberculosis and pricing of alcohol as a means to discourage excessive drinking.     

Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty

Rotor blades are the major components of an aircraft turbine. Their reliability seriously affects the overall aircraft turbine security. Failure mode and effects analysis (FMEA), especially, the risk priority order of failure modes, is essential in the design process. The risk priority number (RPN) has been extensively used to determine the risk priority order of failure modes. When multiple experts give different risk evaluations to one failure mode, which may be imprecise and uncertain, the traditional RPN is not a sufficient tool for risk evaluation. In this paper, the modified Dempster–Shafer (D–S) is adopted to aggregate the different evaluation information by considering multiple experts’ evaluation opinions, failure modes and three risk factors respectively. A simplified discernment frame is proposed according to the practical application. Moreover, the mean value of the new RPN is used to determine the risk priority order of multiple failure modes. Finally, this method is used to deal with the risk priority evaluation of the failure modes of rotor blades of an aircraft turbine under multiple sources of different and uncertain evaluation information. The consequence of this method is rational and efficient.     

An Analytical Model to Measure the Effectiveness of Safety Management Systems: Global Safety Improve Risk Assessment (G-SIRA) Method

The ever-increasing complexity of production systems, together with the need to obtain efficient processes with limited costs, has led companies to develop custom tools for process control and management. Even for risk assessment, the traditional models often are overcome by methods that are best suited to specific needs. In this context, the aim of this paper was to propose a new model, which we call the global safety improve risk assessment (G-SIRA). This model can classify risks and identify corrective actions that allow the best risk reduction at the lowest cost. The proposed model, which is based on improvements to previous research, uses the analytic hierarchy process approach to develop a valid and simple tool for risk management. The G-SIRA method has been tested in a real-world application, i.e., it was applied to all of the processes of a textile company, and the results were compared with those obtained from the classical approach failure mode, effects, and criticality analysis. The comparison clearly showed the effectiveness of the proposed model.