基于油气管道可靠性分析的冲突证据处理研究

该文阐述了故障树模糊分析法,即在引入了相关证据冲突情况下的证据合成法则,建立证据冲突情况下基于故障树的油气管道失效概率计算模型。研究结果表明,D-S证据理论奠定了多专家信息融合的基础,可以令专家组的综合判断结果更接近真实情况。运用D-S证据理论可以解决专家组对同一事件出现判断冲突的问题,从而避免了专家评判结果的片面性,达到统一评判结果的目的。运用基于故障树的油气管道失效概率计算模型整合多位专家的评判信息,可以避免权重分配,实现结果的同等性。     

基于改进证据理论的故障诊断方法研究及应用

针对传感器证据信息全局修正针对性不强及冲突证据无法判别等问题,提出基于改进证据理论的故障诊断方法。利用冲突证据判据判断可信证据与冲突证据,保留可信证据,通过可信度对冲突证据进行针对性修正。该判断及局部修正可降低传感器证据信息的不确定性,并减弱冲突信息对合成结果影响。结合神经网络建立特征空间到证据空间的映射,有效利用网络输出结果,通过信息熵构建原始证据。将所有证据用改进D-S公式合成。通过齿轮泵早期故障试验,与神经网络及其它证据合成方法对比表明,该方法诊断精度较高,从而验证了融合方法的有效性。     

基于Griffith理论岩石裂纹扩展的可靠度分析

先将Griffith理论中的强度参数视为随机变量,给出了相应的裂纹扩展概率解析解;然后将荷载和强度参数均视为随机变量,建立了相应的裂纹扩展概率分析模型和计算公式。利用上述两种计算方法分别研究了不同参数的变异性对裂纹扩展概率的影响。结果表明:参数的变异性对裂纹扩展概率的影响不尽相同,其中第一主应力和岩石抗拉强度的变异性对裂纹扩展概率的影响较明显。     

基于证据理论的结构损伤识别研究

基于多源损伤信息可以有效解决由多种不确定性所导致的损伤误判问题。将证据理论作为理论基础，研究基于多源信息的损伤识别方法。为了充分体现不同证据在重要性、可靠性方面的差异，在考虑实际损伤识别过程的基础上，提出了基于先验和后验可靠性联合校正的证据合成方法。损伤识别试验研究表明，所提出的证据合成方法优于经典的D—S合成法则；相对于传统的基于单一损伤信息的损伤识别而言，基于多源损伤信息能够有效降低损伤误判的可能性，获得准确性、鲁棒性更好的损伤识别结果。     

基于证据理论的断路器机械故障诊断方法

研究了证据理论在高压断路器故障诊断中的应用。探讨了D-S证据理论的有效性问题,并在失效性情况下提供了一种证据理论的改进方法以解决D—S算法在实现过程中存在的证据冲突问题。通过仿真实验,对该方法的有效性进行了验证。     

基于拒绝抽样算法的结构体系可靠度更新

该文着重研究基于观测信息的结构体系可靠度更新模型及其拒绝抽样算法。基于Bayesian理论建立考虑观测信息的结构体系失效概率更新模型,根据观测信息事件类型建立不等式和等式观测信息条件下随机变量的似然函数并推导其后验概率密度函数;基于观测信息域确定随机变量后验样本的拒绝抽样策略,探究拒绝抽样算法的抽样效率,推导更新后结构体系失效概率估计值及其标准差的计算公式;将上述方法应用于刚架结构发生塑性失效时体系可靠度更新计算。研究表明:考虑观测信息的结构体系条件失效概率更新模型可转化为随机变量后验概率密度在失效域上的积分,构造满足观测信息域的先验样本作为随机变量后验样本的抽样策略是可行的,该抽样策略可以处理多随机变量、多观测信息条件下结构体系可靠度更新;与抗力相关随机变量检测值增大及验证荷载值提高均可以降低更新后结构体系的失效概率,与抗力相关的随机变量还需控制其检测误差的标准差,以降低观测信息的不确定性。     

混凝土疲劳损伤强度可靠度置信限分析

以三参数Weibull概型为基础,结合大量C15混凝土试件的试验和某混凝土重力坝的原型观测反演结果,建立了描述高应力水平区域的混凝土在较低损伤量下低周疲劳强度的置信限分析模型,得到了具有给定置信度的疲劳强度、疲劳寿命和疲劳损伤三者间的关系曲线,NSP---g和DSP---g曲线。利用本文方法可方便地在给定置信限内计算混凝土在给定应力水平条件下的疲劳寿命、损伤量和剩余强度, 对混凝土做考虑损伤的可靠度设计,以及对已建结构进行可靠度评价。     

基于D-S证据理论和AHP的故障诊断方法研究

提出了一种基于D-S证据理论和AHP的故障诊断方法，给出了基于AHP法的基本概率分配构造方法．最后，结合故障诊断实例，详细阐述了该诊断方法的应用过程，在对多个传感器提供的证据进行合成后，诊断结论的可信度明显提高，不确定性明显降低，证明该诊断方法合理有效．     

强度理论的发展和展望

强度是各种地上、地下、水下和上天结构的共同的最基本要求.强度理论是研究材料在复杂应力下屈服和破坏规律的学科.由于各种土木、水利、机械、航空、军工等工程结构中的材料,大多处于复杂应力作用下,因此强度理论得到广泛的研究和应用.强度理论是各种工程结构强度计算和设计必需的基础理论.它们也是固体力学、材料强度学研究从弹性到塑性,从弹性到软化或硬化,从弹性到脆塑性,以及从线性到非线性的开始.对材料在复杂应力状态下强度理论(屈服准则、破坏准则等)的发展进行了总结.给出了80多种准则的方程式,反映出强度理论研究的\     

基于连通可靠度约束的分簇算法研究

在研究了一些分簇算法基础上,提出基于连通可靠度约束的、适合大规模随机部署的快速成簇算法。仿真表明基于连通可靠度约束的快速成簇算法得到的分簇覆盖面广、簇头分布合理、稳定性强,与最小ID分簇及优化的最大连接数分簇算法相比,得到簇头数量少,分簇更合理,各成员节点与簇头的连通可靠度好,能保证网络的稳定性与健壮性,大大减少重构开销带来的通信代价,有利于均衡网络能量消耗,延长网络生命周期。     

An effective evidence theory-based reliability analysis algorithm for structures with epistemic uncertainty

The purpose of this article is to develop an effective method to evaluate the reliability of structures with epistemic uncertainty so as to improve the applicability of evidence theory in practical engineering problems. The main contribution of this article is to establish an approximate semianalytic algorithm, which replaces the process of solving the extreme value of performance function and greatly improve the efficiency of solving the belief measure and the plausibility measure. First, the performance function is decomposed as a combination of a series of univariate functions. Second, each univariate function is approximated as a unary quadratic function by the second-order Taylor expansion. Finally, based on the property of the unary quadratic function, the maximum and minimum values of each univariate function are solved, and then the maximum and minimum values of performance function are obtained according to the monotonic relationship between each univariate function and their combination. As long as the first- and second-order partial derivatives of the performance function with respect to each input variable are obtained, the belief measure and plausibility measure of the structure can be estimated effectively without any additional computational cost. Two numerical examples and one engineering application are investigated to demonstrate the accuracy and efficiency of the proposed method.     

Unified reliability analysis by active learning Kriging model combining with random‐set based Monte Carlo simulation method

Reliability analysis with both aleatory and epistemic uncertainties is investigated in this paper. The aleatory uncertainties are described with random variables, and epistemic uncertainties are tackled with evidence theory. To estimate the bounds of failure probability, several methods have been proposed. However, the existing methods suffer the dimensionality challenge of epistemic variables. To get rid of this challenge, a so‐called random‐set based Monte Carlo simulation (RS‐MCS) method derived from the theory of random sets is offered. Nevertheless, RS‐MCS is also computational expensive. So an active learning Kriging (ALK) model that only rightly predicts the sign of performance function is introduced and closely integrated with RS‐MCS. The proposed method is termed as ALK‐RS‐MCS. ALK‐RS‐MCS accurately predicts the bounds of failure probability using as few function calls as possible. Moreover, in ALK‐RS‐MCS, an optimization method based on Karush–Kuhn–Tucker conditions is proposed to make the estimation of failure probability interval more efficient based on the Kriging model. The efficiency and accuracy of the proposed approach are demonstrated with four examples. Copyright © 2016 John Wiley & Sons, Ltd.     

基于模糊证据理论的船舶安全评估

针对船舶安全评估过程中的诸多不确定因素，提出了一种基于模糊证据理论的安全评估方法,将证据体空间中的事件视为模糊子集，并引入模糊概率，更好地描述了专家意见（或知识），给出了质量分配函数的表示．最后，结合某船舶系统，给出了其安全评估的具体实现过程．结果表明，该方法可靠、有效.     

基于BP神经网络的D-S证据理论及其应用

命题基本概率分配（BPA）的确定是D-S证据理论得以广泛应用的关键之一。目前，大部分确定方法受专家知识偏好影响较大，难以反映客观情况。将BP网络运用到基本概率分配的确定过程中，使得BP网络和D-S证据理论两者有机地联合应用，这样既可利用D-S证据理论来表达和处理不确定信息，又可以充分发挥BP网络的自学习、自适应和容错能力。文中建立了基于BP网络的D-S证据理论的故障诊断模型，并给出了证据的融合算法。仿真实验表明，该模型可行。     

基于证据理论的常规导弹阵地生存防护研究

着眼于阵地生存防护时策分析中的不确定性，提出了一种基于证据理论判别来袭武器类型的方法，通过对武器威胁度的评估，建立了来袭武器威胁等级判据，构建了详细的生存对策，可为常规导弹阵地生存防护决策提供有效的辅助支持．     

A virtual age model based on a bathtub shaped initial intensity

This paper presents a new reliability model for complex repairable systems, which combines a bathtub shaped ageing and imperfect maintenance. A bathtub shaped initial intensity function allows to take into account the burn-in period, the useful life and wear out of the systems. Repair effect is expressed by a reduction of the system virtual age, which depends on the ageing of the system. The main characteristics of the model are derived. The most important one is that the maintenance efficiency allows an extension of the system useful life duration. A statistical analysis of the model and an application to real failure data are presented.     

基于组合算法的金属铣削毛刺预测

利用最优权值系数,将灰色理论、人工神经网络和遗传算法有机结合,构建组合算法,依据3种数学方法建立3种组合模型：组合算术平均模型、组合平方和平均模型以及组合比例平均模型,并分别将3种组合模型应用于45钢铣削毛刺的预测．利用3个预测误差评价指标,即平方和误差指标、平均绝对误差指标和平均相对误差指标,对各模型的预测结果进行分析计算.结果表明,组合算术平均模型所得结果与实验结果取得了较好的吻合,具有较高的精度和稳定性,对于金属铣削毛刺的预测具有实际的应用价值．     

Probabilistic modeling of threshold stress intensity factor for fatigue endurance reliability prediction

This study develops a probabilistic model for the threshold stress intensity factor range, which is a critical parameter in infinite fatigue life design under material flaws. The model is based on the proposed concept of probability of propagation in the probabilistic framework, allowing for deriving the probability density function of the threshold intensity factor range. The uncertainty in fatigue crack growth can naturally be incorporated into the resulting distribution. By further introducing the derived distribution into the Kitagawa–Takahashi diagram, the fatigue endurance reliability model can be established in a rational manner. With the first-order asymptotic approximation, the analytical form of fatigue endurance reliability index is obtained. The usefulness of the overall method is demonstrated using realistic engineering application examples.