功能化氧化石墨烯/聚氨酯-环氧树脂复合材料的制备及其性能

利用氧化石墨烯(GO)表面的羟基分别与硅烷偶联剂KH550、KH560反应,制备功能化氧化石墨烯(KH550-GO、KH560-GO),分析了GO功能化前后的微观结构变化;通过溶液插层法将GO加入到聚氨酯-环氧树脂(PU-EP)基体树脂中制备GO/PU-EP复合材料,并对其拉伸性能及热性能进行测试。研究结果表明,KH550、KH560成功对GO进行了功能化,并且与PU-EP复合材料相比,GO/PU-EP复合材料的拉伸性能和热性能均有明显的提高。其中,KH550-GO的加入对基体树脂力学性能和热性能的改善尤为明显。添加0.1wt%的KH550-GO,基体树脂拉伸强度和拉伸模量分别提高了39.0%和94.4%,同时初始热分解温度提高了12℃。     

基于贝叶斯网络的用户需求分析方法研究

目的 尝试在设计研究中引入贝叶斯网络分析工具,结合案例的统计数据计算,分析用户需求与行为的映射关系,系统设计快递包装回收的解决方案。方法 通过调查问卷法、观察法、访谈法对用户行为进行数据采集,结合生活方式转变机制模型,运用贝叶斯网络进行用户研究,产出需求理解,指导产品服务系统设计工作的开展。结果 快递包装再设计的产品基础属性、使用便捷性、系统完整程度会在很大程度上影响用户行为可持续性以及设计认同度。结论 通过研究形成了可持续的快递包装使用系统,帮助引导用户可持续行为,从产品全生命周期的理论角度为快递包装回收的产品服务系统设计提供了面向用户研究的全新设计思考方法,证明了贝叶斯网络在用户研究中解决不确定性问题的积极意义。     

氧化石墨烯改性聚(丙烯酸/丙烯酰胺)高吸水树脂的制备及性能研究

为得到综合性能较优的高吸水性树脂,研究了氧化石墨烯(GO)对聚(丙烯酸/丙烯酰胺)[P(AA/AM)]高吸水树脂的改性作用。首先通过Hummers法制备了GO,采用水溶液合成法合成了P(AA/AM)-GO高吸水树脂,采用SEM、FT-IR、TGR等手段分析了改性高吸水树脂的微观结构及热性能,并对其进行了溶胀测试和保水性能测试;研究了GO质量浓度、单体配比、交联剂用量、引发剂用量等因素对P(AA/AM)-GO的耐盐性、吸水性的影响。结果表明,GO改性P(AA/AM)后,所得树脂的吸水倍率(QW)和吸盐水倍率(QS)分别为502g/g和122g/g,与市场上广泛使用的P(AA/AM)相比,耐盐性提高了2.8倍,热稳定性提高了58%,在100℃失水60min时保水率提高了21.5%。所以GO的加入能有效改善传统高吸水树脂的综合性能,同时拓宽了GO的应用领域。     

基于贝叶斯网络直升机操纵系统的故障诊断

该文针对直升机飞行操纵系统的故障诊断,讨论了故障树分析法和贝叶斯网络法的优缺点,提出一种基于贝叶斯网络的故障诊断方法。首先介绍了贝叶斯网络的基本原理,重点讲述了贝叶斯网络的构建方法和故障诊断流程,应用基于贝叶斯网络的故障诊断方法既可以通过操纵系统部件故障率推断系统总故障率,也可以分析操纵系统的薄弱环节,最后通过实例验证了贝叶斯网络法在直升机飞行操纵系统故障分析中的有效性。     

TiO_2-GO的制备及TiO_2-GO/环氧树脂涂层的抗腐蚀性能

为了提高环氧树脂涂层的抗腐蚀性能,首先,利用改进的Hummers法制备了氧化石墨烯(GO)。然后,将3-氨基丙基三乙氧基硅烷(KH550)改性纳米TiO2负载在GO表面,制备了改性纳米TiO2与GO的复合颗粒(TiO2-GO),通过FTIR、XRD和SEM对TiO2-GO进行了表征。最后,将TiO2-GO分散于环氧树脂中,分别制备出TiO2-GO含量为1wt%、2wt%和3wt%的TiO2-GO/环氧树脂涂层及纯环氧树脂涂层,通过SEM观察了涂层断面形貌,利用电化学工作站和高温高压抗腐蚀测试表征了涂层的防腐蚀性能。结果表明:纳米TiO2通过化学键与GO结合在一起,将TiO2-GO分散于环氧树脂涂层中可以显著提高环氧树脂涂层的抗腐蚀性能。研究为通过添加GO的方法改善环氧树脂涂层的防腐性能提供了参考。     

改性氧化石墨烯对环氧树脂基复合材料热膨胀系数影响的研究

环氧树脂(EP)热膨胀系数(CTE)为65×10~(-6)℃~(-1),碳纤维(CF)CTE为-12×10~(-6)℃~(-1),因此降低EP的CTE是提高碳纤维增强环氧树脂(CF/EP)复合材料低温使用性能的关键。采用氧化石墨烯(GO)和四氧化三铁改性氧化石墨烯(Fe_3O_4-GO)修饰EP,研究了GO及Fe_3O_4-GO对EP基体CTE的影响。结果表明:由于Fe_3O_4-GO表面的官能团可与EP基体形成共价键,从而加强了与EP基体的界面作用;相对于纯EP,GO和Fe_3O_4-GO改性EP的玻璃化转变温度(Tg)分别升高了3.71℃和5.74℃;相对于纯EP,GO和Fe_3O_4-GO改性EP在Tg下的CTE值分别降低了23.77%和33.61%,但在Tg上的CTE值均高于纯EP。     

生物基没食子酸环氧树脂/氧化石墨烯纳米复合材料的制备及热性能

以植物多元酚没食子酸为原料制备了生物基没食子酸环氧树脂(GAER),采用改进的Hummers法制备了氧化石墨烯(GO),并利用硅烷偶联剂KH550对氧化石墨烯进行改性修饰。以琥珀酸酐为固化剂,制备了GAER/KH550-GO生物基纳米复合材料。对所制备的GO及其与GAER的复合材料进行了结构表征和性能测试。结果表明,KH550改性的氧化石墨烯已经剥离形成具有单片分布的结构,GAER/KH550-GO复合材料的储存模量(E′)、玻璃化转变温度(Tg)均随着KH550-GO含量的增加先增加后降低,在KH550-GO的质量分数为0.25%时,Tg提高了21℃,50℃时E′增加了72.2%;在KH550-GO的质量分数为0.75%时,复合材料具有较高的交联密度及耐热性。     

一体式托架缓冲防护结构设计与有限元分析

目的 为解决快递末端配送中存在的回收体系不完善,快递的不安全性,快递员与消费者沟通少,快递员拒绝上门配送等问题。方法 通过层次分析法建立快递末端配送体系的模型,并通过模糊综合评价,得出消费者对于目前快递末端配送的评分。结果 环境效益的权重为0.665,表明消费者较为重视,然而经济效益的权重为0.104,表明消费者较易忽视。同时,建立了快递末端配送综合效益与指标体系的关系式,时间成本的权重最高为0.619,运输成本的权重最低为0.097,表明消费者较为注重时间成本,较易忽视运输成本。综合评价得出消费者对现有快递末端配送的评分结果为75.09分,满意度为中等。结论 提出了快递市场规范化,制定了快递回收机制,合理分配快递末端配送投入方式以及给出了一些APP或小程序的优化建议,为我国快递末端配送提供借鉴。     

基于贝叶斯网络的自由场地震液化沉降评估

基于贝叶斯网络方法,综合考虑地震参数、土体参数和场地条件等12个影响因素,结合场地的液化势和液化潜能指数,建立了地震液化沉降的贝叶斯网络评估模型。通过算例分析,与径向基神经网络方法和IY简化计算方法的评估性能对比,发现地震液化沉降的贝叶斯网络评估模型的优势明显;该模型不仅有较好的评估精度和可靠性,而且还可以进行逆向因果推理。对两个机器学习模型进行敏感因素分析发现,在12个影响因素中,地表峰值加速度、地震持续时间和标准贯入锤击数为较敏感因素,和IY简化算法考虑的参数基本一致。     

GO/TLCP/PF混杂复合材料的热性能和动态力学性能

采用熔融挤出法将热致性液晶聚合物(TLCP)与酚醛树脂(PF)熔融挤出,分别加入氧化石墨烯(GO)、KH550改性GO(KH550-GO)、KH560改性GO(KH560-GO),制备出GO/TLCP/PF混杂复合材料,研究GO的加入对GO/TLCP/PF混杂复合材料的热性能、力学性能、动态力学性能、蠕变和应力松弛的影响。结果表明:GO的加入可提高GO/TLCP/PF混杂复合材料的热性能、力学性能以及动态力学性能;仅加入1%KH560改性的GO,GO/TLCP/PF混杂复合材料的冲击强度比PF复合材料提高了25.6%,储能模量提高了28.1%,蠕变和应力松弛性能也得到改善。其原因是,GO与TLCP具有一定的协同增强效应。     

Reliability assessment for failure-dependent and uncertain systems: A Bayesian network based on copula method and probability-box

Managing failure dependence of complex systems with hybrid uncertainty is one of the hot problems in reliability assessment. Epistemic uncertainty is attributed to complex working environment, system structure, human factors, imperfect knowledge, etc. Probability-box has powerful characteristics for uncertainty analysis and can be effectively adopted to represent epistemic uncertainty. However, arithmetic rules on probability-box structures are mostly used among structures representing independent random variables. In most practical engineering applications, failure dependence is always introduced in system reliability analysis. Therefore, this paper proposes a developed Bayesian network combining copula method with probability-box for system reliability assessment. There are four main steps involved in the reliability computation process: marginal distribution identification and estimation, copula function selection and parameter estimation, reliability analysis of components with correlations and Bayesian forward analysis. The benefits derived from the proposed approach are used to overcome the computational limitations of n-dimensional integral operation, and the advantages of useful properties of copula function in reliability analysis of systems with correlations are adopted. To demonstrate the effectiveness of the developed Bayesian network, the proposed method is applied to a real large piston compressor.     

Human reliability analysis for manned submersible diving process based on CREAM and Bayesian network

The diving mission of manned submersibles is a long‐term, high‐intensity work that is affected by many factors and is in a narrow confined space. In order to improve the reliability of oceanauts' safe operations, this paper is based on the cognitive reliability and error analysis method (CREAM) and the Bayesian network method to study the human errors of the diving mission. First, we construct a Bayesian network framework of the diving process by analyzing the diving steps. Second, the CREAM is applied to calculate the prior probability of each root node's error. Then, the backward reasoning ability of the Bayesian network is used to calculate the posterior probabilities and identify the top few risk nodes. Finally, we obtained the top few risk factors. Among them, we find that the light distribution design in the risk nodes is the more influential risk factor, so a brief design is made on them.     

Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis

This paper deals with the use of Bayesian networks to compute system reliability. The reliability analysis problem is described and the usual methods for quantitative reliability analysis are presented within a case study. Some drawbacks that justify the use of Bayesian networks are identified. The basic concepts of the Bayesian networks application to reliability analysis are introduced and a model to compute the reliability for the case study is presented. Dempster Shafer theory to treat epistemic uncertainty in reliability analysis is then discussed and its basic concepts that can be applied thanks to the Bayesian network inference algorithm are introduced. Finally, it is shown, with a numerical example, how Bayesian networks’ inference algorithms compute complex system reliability and what the Dempster Shafer theory can provide to reliability analysis.     

A new reliability analysis method for repairable systems with multifunction modes based on goal‐oriented methodology

This paper proposes a new systematic reliability analysis method for repairable systems with multifunction modes based on the goal‐oriented (GO) method. First, we create a new function GO operator, a new logical GO operator, and a new auxiliary GO operator, deduce their GO operation formulas, and propose some new rules of the GO operation and an exact algorithm with shared signal of the GO method for such systems. Then, we formulate the analysis process of repairable systems with multifunction modes based on the new GO method. Finally, we apply this new GO methodology to reliability analysis of the control system for a heavy vehicle. To verify the feasibility, advantage, and reasonableness of the new GO methodology, we compare its analysis results with those of fault tree analysis and Monte Carlo simulation. We show that the proposed GO method has clear advantages in system reliability modeling and analysis. All in all, this study not only improves the theory of the GO method and widens its application but also provides a new approach for conducting reliability analysis of complex systems quickly and efficiently.     

Predictive maintenance of complex system with multi-level reliability structure

Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system reliability has been extensively studied with the assumption that systems are either single-component systems or they have a deterministic reliability structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system reliability structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system reliability and the inter-time–slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level, so PdM can be scheduled accordingly.     

Reliability Analysis Method on Repairable System with Standby Structure Based on Goal Oriented Methodology

This paper presents a reliability analysis method on repairable system with standby structure based on goal oriented (GO) methodology. Firstly, a new combination of GO operator, which is composed of a new logical GO operator named Type 18A operator and a new auxiliary GO operator named Type 20 operator, is created to represent standby mode. The availability formula of standby equipment with translation exception is deduced based on Markov process theory. Then, the application method of combination of GO operator for standby mode and the analysis process of repairable system with standby structure based on GO method are proposed. Thirdly, this new combination of GO operator is applied in availability analysis of the hydraulic oil supply system of power‐shift steering transmission. Finally, the results obtained by the new GO method are compared with the results of fault tree analysis, Monte Carlo simulation, GO methods using Type 2 operator and Type 18 operator to represent the standby mode, respectively. And the comparison results show that this new GO method is applicable and reasonable for reliability analysis of repairable system with standby structure. All in all, this paper provides guidance for reliability analysis of repairable systems with standby structure. Copyright © 2015 John Wiley & Sons, Ltd.     

Reliability technology using GO methodology: A review

Goal‐oriented (GO) methodology is a graphic inductive method mapping the system structure, its function constitute, characteristics, and working principle visually based on decision tree. Recently, the studies on reliability technologies for GO method of complex systems have been gotten a lot of attention by scholars because of the obvious advantages of modeling and analysis power. Indeed, the GO method has been applied in reliability and safety analysis of various areas, such as transportation and power systems, to prove its value. Thus, this paper provides a brief review of the GO method from aspects of providing its development history and its engineering applications and expounding its development of the theory of the GO method. At last, the future directions of GO method are discussed.     

A new reliability analysis method for repairable systems with closed‐loop feedback links

A new reliability analysis method for repairable systems with closed‐loop feedback link (CLFL) is proposed based on GO methodology. A method for creating new function GO operators is used to describe the CLFL. Next, methods for deducing the formulae of the new GO function are proposed. In addition, a 2‐level GO model is proposed for the GO operation of repairable systems with CLFL. And then, quantitative and qualitative analysis methods for repairable systems with CLFL based on the GO method are proposed, and a process for analyzing repairable systems with CLFL based on the new GO method is formulated. Finally, we used this new GO method to analyze the reliability of an electro‐hydraulic servo speed control system and a power‐shift steering transmission control system for a heavy vehicle. To verify the feasibility, advantages, and reasonability of the new GO method, we compared our results with those obtained by fault tree analysis, Monte Carlo Simulation, and an existing GO method using serial and parallel structures to represent the CLFL. All in all, the proposed method overcomes the limitations of the existing methods as well as increasing its applicability. And it provides a new approach for reliability analysis of repairable systems with CLFL.     

考虑企业效益下的校园快递包装回收系统仿真研究

目的研究考虑企业效益下高校快递包装回收系统的系统优化问题,以改善高校校园因快递包装造成的环境污染。方法针对大学校园快递包装的回收特点,应用第三方企业回收的包装回收模式,引入包装破损率对包装进行分类回收,考虑再生效益对回收系统的影响,利用系统动力学方法建立校园环境下的快递包装回收模型。进一步,从快递包装平均重量与校企合作水平出发,设计4个不同仿真方案,通过VensimPLE仿真软件对模型进行验证,分析了影响快递包装回收效果的主要因素,并对主要影响因素进行了敏感性分析。结果实例验证可知,仅减小快递包装的平均重量对于高校快递包装回收现状改善效果不明显。通过增大校企合作水平影响因子、简化快递包装、提升学生环保意识相结合,可使校园被废弃包装量、废弃包装填埋量降低,企业回收效益明显,校企合作更加紧密。结论提高校企合作水平可以显著改善快递包装的回收效果,提升企业效益,同时减少包装平均重量可以有效控制校园废弃包装的累积速率与填埋量,对于发展绿色物流具有重要意义。     

基于AHP-熵权法的货舱码垛效果模糊综合评价

目的 解决快递包裹物流过程中暴力分拣问题的前提是对暴力分拣行为进行有效识别,为此提出一种基于加速度分布特征的快递包裹暴力分拣行为智能识别方法。方法 利用集成三轴加速度传感器的数据采集设备实时采集并截取潜在异常操作情况下快递包裹的加速度数据。然后将潜在异常数据上传服务器,在服务器端执行分布特征的提取。最后将特征矩阵送入神经元网络分类器,得到当前包裹所受操作类别的结果。结果 实验证明,文中提出的多阈值截取方法可以有效截取潜在异常数据,使用加速度分布作为特征可以有效对暴力分拣行为进行分类。其中,使用BP网络作为模式识别分类器时,分类正确率可以达到93.6%;使用CNN作为模式识别分类器时,分类正确率可以达到95.3%。结论 文中提出的暴力分拣识别方法准确、快速,具有良好的在线实时性。基于此方法可以构建暴力分拣行为识别数据库,为进一步完善快递企业服务水平定量化评价体系提供数据支撑。这些数据还可以用于对暴力分拣产生的原因进行深入分析,从而提出减少暴力分拣行为的针对性解决方案。