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It is the complexity and the calculated quantities which require the use of the general algorithm for evaluating the maximum flow of the network. The logical cutting tree algorithm model of the network maximum flow is built using both the logical thought of the fault tree method and cutting set theories of the graphics. Therefore, the problem is resolved well and its calculation process is simpler and its logic is better than the original algorithm method.

This paper aims to propose a method of hazards identification of uncontained engine rotor failure (UERF) based on collision detection between geometric models. UERF is a typical particular risk that imposes threat on flight safety of an aircraft. Aircraft systems are made up of many parts and components; therefore, it is difficult to identify hazards caused by UERF early in the design cycle.

The methodology involves the following steps: the mapping relationship of input information is established; the parametric models are used to simulate the uncontained fragments of different categories; the parts and components that the uncontained fragment may collide with are determined by uniform space decomposition and precise collision detection; and the catastrophic hazards are identified with the comparison of the collision detection result sets and the minimum cut sets.

An application case, which takes the hydraulic system of a certain type of civil aircraft in design as a study object, shows that the method proposed in this paper is suitable and efficient for hazards identification of UERF.

The method proposed herein is useful for acquiring the minimum cut sets that will be triggered by the uncontained fragment in the design phase.

A novel and effective method of hazards identification of UERF for an aircraft with large and complex systems is presented, which is helpful to the optimization of the layout design of parts and components of the aircraft.

The purpose of this paper is to determine the optimum level of geometrical parameters such as helix angle, nose radius, rake angle and machining parameters such as cutting speed, feed rate and depth of cut to arrive minimum surface roughness and tool wear during end milling of Al 356/SiC metal matrix composites (MMCs) using high speed steel end mill cutter.

L27 Taguchi orthogonal design with six factors and three levels is employed for conducting experiments. Analysis of variance (ANOVA) is carried out using Minitab16 software to find the influence of each input parameter on output performance measure. Grey-fuzzy logic multi optimisation algorithm is used to find the optimum level of the input parameters for minimum surface roughness and tool wear simultaneously.

It is found that optimal combination of helix angle 40°, nose radius 0.8 mm, rake angle 12°, cutting speed 90 m/min, feed rate 0.04 mm/rev and depth of cut 1.5 mm have generated minimum surface roughness of 0.4063 µm and tool wear of 0.0375 mm. From ANOVA analysis, it is found that cutting speed influence is more on output performance followed by helix angle and rake angle compared with other machining and geometrical parameters.

The influence of tool geometry during end milling of MMC using Grey-fuzzy logic algorithm has not been explored previously.

The purpose of this paper is to present an efficient, interactive foreground/background image segmentation method using mean shift (MS) and graph cuts, in order to improve the segmentation performance with little user interaction.

By incorporating the advantages of the mean shift method and the graph cut algorithm, the proposed approach ensures the accuracy of segmentation results. First, the user marks certain pixels as foreground or background. Then the graph is constructed and the cost function composed of the boundary properties and the region properties is defined. To obtain the hidden information of user interaction, the foreground and background marks are clustered separately by the mean shift method. The region properties are determined by the minimum distances from the unmarked pixels to the foreground and background clusters. The boundary properties are determined by the relationship between the unmarked pixels and its neighbor pixels. Finally, using the graph cuts method solves the energy minimization problem to get the foreground which is of interest.

The paper presents experimental results and compares the results to other methods. It can be seen from the comparison that this method can obtain a better segmentation performance in many cases.

The paper incorporates the advantages of the mean shift method and the graph cut algorithm to obtain better segmentation results, even though the scene is complex.

System reliability optimisation in today’s world is critical to ensuring customer satisfaction, businesses competitiveness, secure and uninterrupted delivery of services and safety of operations. Among many systems configurations, complex systems are the most difficult to model for reliability optimisation. The purpose of this paper is to assess the performance of a novel optimisation methodology of the authors, developed to address the difficulties in the context of a gas carrying system (GCS) exhibiting dual failure modes and high initial reliability.

The minimum cut sets involving components of the system were obtained using the fault tree approach, and their reliability constituted into criteria which were maximised and the associated cost of improving their reliabilities minimised. Pareto optimal generic components and system reliabilities were subsequently obtained.

The results indicate that the optimisation methodology could improve the system’s reliability even from an initially high one, granted that the feasibility factor for improving a component’s reliability was very high. The results obtained, in spite of the size (41 objective functions and 18 decision variables), the complexity (dual failure modes) and the high initial reliability values provide confidence in the optimisation model and methodology and demonstrate their applicability to systems exhibiting multiple failure modes.

The GCS was assumed either failed or operational, its parameters precisely determined, and non-repairable. The components failure rates were exponentially distributed and failure modes independent. A single weight vector representing expression of preference in which components reliabilities were weighted higher than cost was used due to the stability of the optimisation model to weight variations.

The high initial reliability values imply that reliability improvement interventions may not be a critical requirement for the GCS. The high levels could be sustained through planned and systematic inspection and maintenance activities. Even so, purely from an analytical stand point, the results nevertheless show that there was some room for reliability improvement however marginal that is. The improvement may be secured by: use of components with comparable levels of reliability to those achieved; use of redundancy techniques to achieve the desired levels of improvement in reliability; or redesigning of the components.

The novelty of this work is in the use of a reliability optimisation model and methodology that focuses on a system’s minimum cut sets as criteria to be optimised in order to optimise the system’s reliability, and the specific application to a complex system exhibiting dual failure modes and high component reliabilities.

To reduce the flammability exposure assessment time and meet the requirements of airworthiness regulations of transport aircraft, inerting system has become the standard configuration of modern civil aircraft. Therefore, airworthiness regulations put forward definite quantitative index requirements for the safety of inerting system, and to obtain the quantitative data of the safety of inerting system, it is necessary to solve the calculation method. As one of the quantitative/qualitative evaluation techniques for system safety, fault tree analysis is recognized by international airworthiness organizations and national airworthiness certification agencies. When fault tree analysis technology is applied to quantitative analysis of the safety of inerted system, there are still some problems, such as heavy margin of constructing fault tree, great difficulty, high requirement for analysts and poor accuracy of solving when there are too many minimum cut sets. However, based on tens of thousands of flight simulation tests, Monte Carlo random number generation method can solve this problem.

In this paper, the fault tree of airborne inerting system is established, and the top event is airborne inerting system losing air separation function. Monte Carlo method based on random number generation is used to carry out system security analysis. The reliability of this method is verified.

The static fault tree analysis method based on Monte Carlo random number generation can not only solve the problem of quantitative analysis of inerting system, but can also avoid the defects of complicated solution and inaccurate solution caused by the large number of minimum cut sets, and its calculation results have good reliability.

The research results of this paper can be used as supporting evidence for airworthiness compliance of airborne inerting system.

The research results of this paper can provide practical guidance for the current civil airworthiness certification work.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

The purpose of this paper is to facilitate perishable product supply chain (PPSC) managers and practitioners to assess PPSC failure events. The paper proposed fault tree methodology for assessing failures associated with PPSC for evaluating the performance in terms of effective PPSC management adoption.

Initially, different failure events were identified from literature and semi-structured interviews from experts. Fault tree model was developed from the identified failure events. Probability of failure events was calculated using Poisson distribution based on the annual reports and interviews conducted from experts. Further, qualitative analysis – minimum cut sets (MCSs), structural importance coefficient (SIC) – and quantitative analysis – Birnbaum importance measure (BIM), criticality importance factor (CIF) and diagnosis importance factor (DIF) – were performed for ranking of failure events. In this study, fault tree development and analysis were conducted on apple supply chain to present the authenticity of this method for failure analysis.

The findings indicate that the failure events, given as failure at production and procurement (A2), that is, involvement of middleman (BE3), handling and packaging failure (BE4) and transportation failure (A3), hold the highest-ranking scores in analysis of PPSC using fault tree approach.

This research uses the modularization approach for evaluation of failure events of PPSC. This paper explores failures related to PPSC for efficient management initiatives in apple supply chain context. The paper also provides suggestion from managerial perspective with respect to each failure event.

The purpose of this paper is to investigate design alternatives for pump‐included water distribution networks considering sustainability and reliability aspects. The aim is to demonstrate that CO₂ emissions could be reduced at a reasonable cost. The paper also investigates the trade‐offs between cost and reliability of water distribution networks.

An existing genetic algorithm optimization tool is customized in this research to perform multi‐objective optimization with various objectives and constraints. The developed model is demonstrated using a benchmark water distribution network.

The results from this research suggest that CO₂ emissions from water distribution networks could be reduced at a reasonable cost by choosing better objectives during the design stage. High system reliability could also be ensured for the lifetime by paying reasonable additional cost. This research presents various design alternatives for an engineer to choose from.

The design of water distribution networks is a computationally complex process and often requires significant CPU time to arrive at an optimal solution. This problem is significant in the case of larger networks, especially when all the failed states need to be simulated. Simpler measures of reliability could be adopted in the future.

Although a significant amount of research had been undertaken in the area of optimal water distribution network design, only limited research includes environmental impacts as a design objective. This paper not only includes environmental aspects but also considers reliability. The model proposed in this research is a useful tool for engineers for considering various alternatives before choosing the best design.