铝型材挤压模具的失效分析

根据铝型材挤压模具的服役条件,采用宏观与微观相结合的分析方法,分析现场挤压模具的失效形式、失效性质和失效原因,并提出了提高模具使用寿命的一些措施.     

Selection of failure frequency and its impact on risk assessment – A case study from plot plan optimisation

Facility Siting is an important phase of project development. A critical stage is plot plan optimisation, where significant potential hazards are eliminated due to equipment spacing. In addition to ensuring appropriate compliance with minimum spacing requirements, occupied building studies to achieve compliance with the requirements of API 752 and API 753 could also be undertaken to optimise safety outcomes. The studies are done in three stages, where the first stage is hazard identification, second stage is consequence assessment and the third stage is risk assessment. Third stage assessments are only carried, if the consequence based siting recommendations are not practical to implement.This paper presents the challenges in estimating risk due to process hazards with a focus on selecting right event likelihood data. A comparison is presented on the variation in predicted risk levels based on equipment failure rates and leak frequencies.Case study of a plot plan optimisation study is undertaken with DNVGL Phast Risk and the variation in risk levels up to two orders of magnitude are recorded. Challenges such as adaption of data for local conditions, consistent definitions of failure, sample size of data, applicability of data play a significant role in identifying and correctly quantifying the risk levels. Such challenges and its impact on risk quantification are presented in this paper as well as its impact on facility siting.     

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.     

Development of a biaxial loading frame for sheet metal

Characterization of the evolving yield loci and forming limit diagrams for sheet materials under biaxial loading is necessary for the development of accurate sheet metal forming process simulations. Biaxial tension testing has been shown to have significant advantages over the current computational and experimental methods for such material characterization; however, the few commercially available loading frames are far too large and expensive to be practical for most metal forming research laboratories. In this paper, the design of a practical servohydraulic biaxial loading frame is presented. The design, control, and operation of the loading frame are discussed in detail, and experimental data is provided to validate the effectiveness of the control system with respect to specimen center shifting.     

System safety assessment using safety entropy

Safety assessment has a primary role in hazardous operations. Most studies on safety assessment focus on risk and accident modeling, in which safety is absent. These top-down methods are highly dependent on the occurred accidents to establish accidental scenarios, which may make the assessment approach lagging behind the evolving modern systems. Moreover, this “special to general” logic is scientifically suspect in safety assessment. There is a call for the development of safety assessment methods in the presence of system safety to complement risk-focused safety analysis. These methods should provide a framework based on a bottom-up approach to examine system safety from the operational perspective. This paper has attempted to provide a potential solution. In particular, a novel concept of safety entropy is proposed to integrate with The Functional Resonance Analysis Method (FRAM), which is used to form the qualitative understanding of a system. A formula consisted of safety entropy, functional conformability, and system complexity has been established to determine the spontaneity of the safety state-changing process. The proposed method is applied to the safety assessment of a propane feed-control system. The results show the applicability of the method. Nevertheless, the model still needs to be further improved to fulfill better support for safety-related decision problems.     

Failure mode and effect analysis using regret theory and PROMETHEE under linguistic neutrosophic context

Failure mode and effect analysis (FMEA), which aims to identify and assess potential failure modes in a system, has been widely utilized in diverse areas for improving and enhancing the performance of systems due to it is a powerful and useful risk and reliability assessment instrument. However, the conventional FMEA approach has been suffered several criticisms for it has some shortcomings, such as unable to handle ambiguous and uncertain information, neglect the relative weights of risk criteria, and without considering the psychological behaviors of decision-makers. To ameliorate these limitations, this paper aims at establishing a hybrid risk ranking model of FMEA via combing linguistic neutrosophic numbers, regret theory, and PROMETHEE (Preference ranking organization method for enrichment evaluation) approach. In the presented model, linguistic neutrosophic numbers are adopted to capture decision-makers’ evaluation regarding the failure modes on each risk criterion. A modified PROMETHEE approach based on regret theory is presented to obtain the risk priority of failure modes considering the psychological behaviors of decision-makers. Moreover, a maximizing deviation model and TOPSIS (Technique for order preference similar to ideal solution) are separately applied to derive the weights of risk criteria and decision-makers. Finally, a numerical example relating to the supercritical water gasification system is employed to implement the presented method, and the effectiveness and feasibility of the proposed model are validated by the results derived from a sensitivity and comparison analysis.     

游乐设施危险源、损伤、失效和故障概念辨析

针对游乐设施行业危险源、损伤、失效和故障四个概念的认知差异,通过研究相关领域这四个概念定义内涵及外延,结合游乐设施安全保障要求高、人-机-环交互频繁、运行工况复杂的特点,重新定义游乐设施这四个概念,辨析相互之间的逻辑关系,并结合游乐设施事故案例进行解析.结果表明:新定义对游乐设施危险源、失效、损伤和故障之间的关系阐述更加清晰准确,为后续的风险评价奠定良好基础.     

煤层底板采动导水破坏深度计算的神经网络方法

在综合分析影响煤层底板采动导水破坏深度因素的基础上 ,应用人工神经网络方法 ,建立了底板破坏深度的计算模型。该模型利用现场观测资料作为学习训练样本和测试样本 ,对模型的测算结果、理论计算值和实测值进行了对比分析。结果表明 :用神经网络方法计算底板破坏深度考虑的因素更加全面 ,结果更接近于实际。笔者研究的计算模型和测算方法 ,为承压水上安全采煤决策提供了科学依据。     

A new pre-assessment model for failure-probability-based-planning by neural network

At present, the prediction of failure probability is based on the operation period for laid pipelines, and the method is complicated and time-consuming. If the failure probability can be predicted in the planning stage, the risk assessment system of gas pipeline will be greatly improved. In this paper, the pre-laying assessment model is established to minimize risk of leakage due to piping layout. Firstly, Fault Tree Analysis (FTA) modeling is carried out for urban natural gas pipeline network. According to expert evaluation, 84 failure factors, which can be determined in the planning stage, are selected as the input variables of the training network. Then the FTA model is used to calculate the theoretical failure probability value, and the failure probability prediction model is determined through repeated trial calculation based on BP (Back Propagation Neural Network) and RBF (Radial Basis Function), for obtaining the optimal network parameter combination. Finally, two prediction models are used to calculate the same example. By comparing our pre-assessment model with the theoretical prediction consequences of the fault tree, the results show that the error of RBF prediction model can be close to 3%, which proves the validity and correctness of the method.     

Corrosion failure probability analysis of buried gas pipelines based on subset simulation

Corrosion is the main reason for the failure of buried gas pipelines. For effective corrosion failure probability analysis, the structural reliability theory was adopted in this study to establish two calculation models for pipeline corrosion failure: the pressure failure model and von Mises stress failure model. Then, two calculation models for the corrosion failure probability were established based on a corrosion depth growth model obtained from actual survey data of soil corrosion characteristics. In an example, Monte Carlo simulation (MCS) and subset simulation (SS) were used to analyze the corrosion failure probability of pipelines, and the results were compared. SS can compensate for the shortcomings of MCS as it has higher computational efficiency and accuracy. Therefore, SS was adopted to simulate variations in the corrosion failure probability of buried pipelines with the service time for the two failure probability calculation models, which were applied to a natural gas pipeline located in a chemical industry park in Zhuhai, China. A sensitivity analysis was carried out on the relevant parameters that affect the failure probability. The results showed that multiple loads caused by the covering soil, residual stress, temperature differential, and bending stress have a non-negligible effect on the pipeline reliability. The corrosion coefficients gradually become the most important factors that affect the failure probability with increased service time. The proposed methodology considers the actual operating conditions of pipelines to provide a reliable theoretical basis for integrity management.