无线传感器网络中的选择转发攻击检测

针对无线传感器网络中现有检测攻击方法需要节点协同或检测硬件的缺点,对选择转发攻击进行研究,提出一种基于信任度与丢包行为的检测方法。通过节点收发数据包的情况,计算节点的信任度,结合节点丢包行为评估,判断节点是否发动攻击。这种检测方法的计算与存储开销都集中于基站,且不需增加额外的检测设备。实验结果表明,这种方法较之现有检测方法,检测率更高。     

Visual Prolog在专家系统故障诊断中的应用

针对故障诊断的特点,利用Visual Prolog语言在知识表示及逻辑推理过程中的优势,以流程工业中易发生事故的催化裂化装置为例设计并实现了基于规则的故障诊断专家系统。用事实和规则这2种构成Visual Prolog语言的短语进行知识库和推理机的编写。通过在催化裂化生产装置的实际应用表明,该诊断系统效果良好,能够对出现的多数异常工况进行有效的推理及分析,及时查找出故障原因,并提供相应的故障处理措施。     

变压器漏磁热损的光纤Bragg光栅检测与温升特性分析

变压器的漏磁场强度随着变压器容量的增大而增大,漏磁场强度越大在变压器各结构中引起的漏磁损耗越大,导致变压器运行效率低下,进而影响变压器的正常运行。对35 kV变压器进行试验,将漏磁产生最大处的温度与油箱产生顶端处的温度进行了对比,试验得出当开风机时该处与油箱顶端处的最高温度差为8.7℃、当负荷降至1倍功率时温差为2.9℃,它们的实时温度曲线图与变压器的运行一致,产生了局部温升的现象。通过光纤Bragg光栅（ FBG）检测温度的变化反映变压器漏磁的情况,从而实现了对变压器漏磁的实时在线监测。     

换流站阀水冷系统主泵轴承故障诊断方法研究

对换流站阀水冷系统主泵轴承的故障诊断方法进行了比较研究.采用支持向量机作为分类工具,分别利用时域特征值和小波包分解取样本熵作为样本训练,比较分类准确率,并择优用于换流站阀水冷系统主泵的轴承故障诊断.首先,利用轴承故障试验台的数据,对采用时域特征值和小波包分解取样本熵作为样本训练的分类准确率进行了比较,结果表明小波包分解取样本熵值比时域特征参数更适合用于特征故障分类.然后将小波包分解取样本熵值用于换流站阀水冷系统主泵的轴承故障诊断,结果显示分类准确率达98％,完全满足工程运用需求.     

面向污水处理的动态变分贝叶斯混合因子故障诊断

在污水生化处理过程中,存在着多变量耦合、强非线性、参数时变、大滞后等特点,面对这些特点,传感器故障频发,从而导致生化过程无法得到有效优化和诊断.为此,本文在结合动态数据特性的基础上提出了一种基于变分贝叶斯混合因子的动态故障诊断方法,同时,利用混合因子的在线调整实现了诊断模型的半自适应化.该方法能够捕捉到污水处理过程的强非线性和动态性,从而可有效降低故障诊断的误报率和漏报率.通过在国际水协会的BSM1模型上的模拟研究,充分表明所提出的策略可以显著提高故障诊断能力,精确地检测传感器的突变和漂移故障,甚至定位故障所发生的根本原因.     

基于多信号模型和盲源分离的复合故障诊断方法

利用多信号模型可简明表征系统因果关系以及盲源分离算法可提取系统本源信息的特点, 提出一种新颖有效的复合故障诊断方法. 首先, 针对复合故障下多信号模型出现冗余测试和故障模糊组的情况, 应用盲源分离算法实现测点信息的盲分离, 基于盲信号重建多信号模型的因果结构; 其次, 理论分析了该方法对复合故障具有良好的可诊断性. 轧制过程AGC系统的实验结果表明, 所提出方法对双复合故障和部分多复合故障的隔离和定位准确率可达100%.

     

基于Rollout算法的测试不可靠条件下的诊断策略*

实际复杂装备系统测试时存在虚警和漏检,其导致的误诊费用往往超过了测试成本。针对复杂系统故障诊断中诊断精度低、虚警率高的问题,进行了不可靠测试条件下,基于Rollout算法的诊断策略优化方法研究。首先建立综合考虑测试点的故障检测能力、信息量、测试费用以及测试结果信任度的启发函数,然后利用Rollout算法生成诊断策略,最后用阿波罗检测系统实例验证了该算法的优越性。理论和实验表明,Rollout算法的平均测试费用低于贪婪算法,而且计算时间在可接受范围内,因此可用于测试不可靠条件下的诊断策略设计。     

改进人工蜂群算法及其在应急调度优化问题中的应用

针对于大规模灾难发生时首批“生命”物资的应急调度建模及优化求解问题,将受灾点缺失损失评价函数由线性扩充到非线性,对一次性消耗类和连续性消耗类物资建立了多对多约束多目标调度模型。基于Pareto支配和拥挤距离的概念将人工蜂群基本算法应用到此问题的求解,并对算法进行了以下改进：基于反向食物源的定义提出融合反向学习的食物源初始化,以提高初始解的质量;将反向学习策略和广泛学习策略融合到蜜蜂搜索过程,以反向食物源和其他较好食物源信息来引导蜜蜂搜索方向。三种规模的仿真实验表明,所提出算法所求出的非支配前沿解集更具多样性,分布更加广泛和均匀,能够为应急物资调度决策进行支持。     

Thermal and cardiovascular strain imposed by motorcycle protective clothing under Australian summer conditions

Motorcycle protective clothing can be uncomfortably hot during summer, and this experiment was designed to evaluate the physiological significance of that burden. Twelve males participated in four, 90-min trials (cycling 30 W) across three environments (25, 30, 35 °C [all 40% relative humidity]). Clothing was modified between full and minimal injury protection. Both ensembles were tested at 25 °C, with only the more protective ensemble investigated at 30 and 35 °C. At 35 °C, auditory canal temperature rose at 0.02 °C min^?1 (SD 0.005), deviating from all other trials (p < 0.05). The thresholds for moderate (>38.5 °C) and profound hyperthermia (>40.0 °C) were predicted to occur within 105 min (SD 20.6) and 180 min (SD 33.0), respectively. Profound hyperthermia might eventuate in ~10 h at 30 °C, but should not occur at 25 °C. These outcomes demonstrate a need to enhance the heat dissipation capabilities of motorcycle clothing designed for summer use in hot climates, but without compromising impact protection.

Practitioner’s Summary:

Motorcycle protective clothing can be uncomfortably hot during summer. This experiment was designed to evaluate the physiological significance of this burden across climatic states. In the heat, moderate (>38.5 °C) and profound hyperthermia (>40.0 °C) were predicted to occur within 105 and 180 min, respectively.     

Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach

It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.