状态最优估计融合算法在伺服系统中的应用

针对如何提高伺服系统多传感器测量数据的可信度,提出了一种状态最优估计融合算法。该算法依据Bayes后验估计理论,把求n个测量数据的状态最优估计转化为求出满足Y(需要测量的状态值)的最大后验概率maxP(Y|E)的估计值Y(E?)的问题,求得多传感器对同一目标的状态测量值的最优值,来提提高伺服测量数据的可信度。该算法应用到具有三种传感器的伺服系统中,实验结果证明,系统能持久稳定、高精度地跟踪运动目标,大大提高了系统的稳定性。     

多特征融合的非线性目标跟踪算法

为克服视频目标跟踪中仅利用单一特征易导致的跟踪失败,提出了一种基于多特征融合的非线性目标跟踪算法。通过灰度直方图来表征目标的总体分布,利用边缘特征来提取目标的高频细节,将两者融合于粒子滤波的概率模型框架中。并提出一种基于半峰宽和贡献度的特征可信度计算方法,动态调节粒子数目,使可信度高的特征拥有较多的粒子。最后,进行了目标跟踪仿真实验,结果表明,该算法具有较强的抗局部遮挡能力,与单特征跟踪算法相比,平均跟踪误差减小了0．5个像素。     

马尔科夫链扫雷效果评估模型可信度验证

目前基于马尔科夫链的扫雷作战效果评估模型从单链到多链，理论模型与算法设计已经架构完毕，但该模型的可信度还有待验证。文中利用误差分布拟合检验的方法，对模型可信度进行验证，计算表明，该模型可信度较好。     

基于数据融合和多重智能模型的目标识别和跟踪

考虑到单传感器的系统存在着局限性,提出了基于多传感器（雷达和红外）信号融合的目标识别和跟踪系统,以利用数据的互补和冗余。特征层融合能利用各传感器提供的特征为提高目标识别能力;对于点目标和面目标分别提出了智能规则推理和神经网分类器的目标识别方法。决策层融合能提高目标跟踪的精度并提高抗干扰性;提出了可信度决策的目标跟踪方法。     

双样本秩检验与基数型可信度模型在葡萄酒评价中的应用

为了正确评价两组葡萄酒评酒员的评价结果有无差异性和谁的可信度更高，本文根据评价结果的独立性、随机性及分存在布近似情况等，利用双样本秩检验方法进行差异性建模分析，运用M．mitab软件的Mann—Whiteny进行最后求解。在可信度的评判上，基于应用实例中的评审专家可信度评价模型，转换为对评酒员的可信度评价模型，探讨了酿酒葡萄多指标性评价，建立了评价的水平排序和分类方法。     

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．     

多传感器信息融合在电机故障诊断中的应用

为了提高电机故障诊断的准确性,引入一种多传感器信息融合的诊断方法。将多个传感器所采集的转子振动频谱信号处理后,利用蚁群神经网络进行故障局部诊断,以获得彼此独立的证据,再由证据理论对各证据进行融合,最终实现对电机故障的准确诊断。实验结果表明,该方法有效提高诊断的可信度,减少电机故障分类识别的不确定性。     

基于灰色关联度的多传感器数据融合方法研究

针对多个传感器对某一特性指标多次测量的数据融合问题,提出一种基于灰色关联度的多传感器数据融合新方法。该方法将各传感器测得的数据视为一个行为序列,利用灰色关联度对不同传感器测得数据之间的接近程度进行度量,并通过灰色关联矩阵全面衡量数据间的综合接近程度,然后根据非负对称矩阵的性质求得各传感器测得数据在数据融合表达式中的权重,从而实现多传感器数据的融合。仿真结果表明应用所提出方法对雷达数据进行处理,可有效降低跟踪误差,提高测量精度。     

多传感器数据融合技术应用研究

当前多传感器数据融合技术中,多是基于支持度矩阵的最优加权多传感器融合方法,但在支持度矩阵的计算中,相对距离的选取多是基于时域峰值,时域峰值在测量中存在较大干扰和不确定性,因此通过时域峰值不能很好地确定两个传感器之间的相互支持程度．本文提出了一种基于互相关函数的支持度矩阵计算方法,然后对支持度高的传感器进行最优加权融合．通过仿真表明：此种方法无需传感器的任何先验知识,能够客观显示各传感器的可靠程度,比其他方法具有更高的融合精度．     

基于层次分析的大洋样品申请可信度的评估模型

针对大洋样品申请审核工作的具体工作要求,提出大洋样品申请可信度的评估模型。本文首先根据大洋样品申请实际工作情况建立影响因素层次化评估模型,然后对提出的层次化模型进行量化分析,最后利用层次分析法（AHP）计算出各影响因素对大洋样品申请可信度的具体影响数值。根据计算的数值进行分析,对大洋样品申请可信度进行评估。     

A novel framework for the reliability modelling of repairable multistate complex mechanical systems considering propagation relationships

Due to the propagation, amplification, and concatenation in a failure process, the reliabilities of repairable multistate complex mechanical systems (RMCMSs) may be affected by a significant fluctuation due to a small exception associated with a reliability indicator. Focused on the problems arising from the lack of propagation relationships among fault modes, functional components, and failure causes in conventional reliability models, a novel framework for reliability modelling is proposed to comprehensively analyse the reliabilities of RMCMSs. First, the reliability models are abstracted as weighted and directed networks with five layers. Second, an improved failure mode and effects analysis (IFMEA) method combined with the D‐number method and VIKOR approach is presented to determine the importance of reliability nodes. Third, a cut set of the reliability model is generated by any exception of a reliability indicator by considering the propagation relationships, and the reliability sensibility index is defined to characterize the fluctuations in system reliability. The effectiveness of the proposed framework is demonstrated in an actual reliability modelling application. As an intuitive method, the proposed framework inherits the advantages of conventional models but overcomes the drawbacks of these existing methods. Therefore, this method can be flexibly and efficiently used in the reliability modelling of RMCMSs. Moreover, the approach provides a foundation for comprehensive and dynamic reliability analysis and the failure mechanism mining of RMCMSs, and it can be used in other engineering applications.     

基于局部平稳法的粘滞阻尼被动控制结构抗震可靠度分析

针对基于确定性激励的被动控制装置参数设计不具有普遍性的问题,提出了粘滞阻尼被动控制结构在一般非平稳随机地震动作用下抗震可靠度分析的局部平稳法。首先基于非平稳随机过程的局部平稳小波模型,提出了适用于临界阻尼比较大的粘滞阻尼被动控制结构的非平稳地震动输入-多自由度（受控）结构位移响应输出的功率谱关系。其次,根据超越过程的Markov过程假定及各阶响应谱矩,得到了受控结构层间位移的动力可靠度。数值分析结果表明：粘滞阻尼器在不同层间的配置,对受控结构的层间动力可靠度有显著影响。最后,以一个6层剪切型多自由度结构为例,对比了Monte Carlo模拟估计与本文所提方法计算的结构动力可靠度,验证了该方法的可靠性与高效性。     

Fault Diagnosis Improvement Using Dynamic Fault Model in Optimal Sensor Placement: A Case Study of Steam Turbine

Health data are collected dominantly through sensors mounted on different locations in the system. Optimization of sensor network has a significant influence on the reliability of system health prognostics process. In this research, the effect of sensors reliability is studied on their placement optimization. Sensors are considered in this study as components in system failure model. This study is aimed to use ‘Priority AND’ gate for evaluating the effect of time dependencies of sensors as well as components failure on the optimal sensor placement. Because of that, PAND gate is added to the fault tree between all sensors and their corresponding components to develop the failure model of each sensor placement scenario. For calculating the probability of top event, a Monte Carlo‐based algebraic approach is proposed. In algebraic approach, temporal operator ‘BEFORE’ is proposed for calculating the probability of ‘PAND’ gate. The result of using ‘BEFORE’ operator is an analytical solution for probability of each cut sequence. Because of the complexity of analytical solution in practical problems, a Monte Carlo simulation is utilized on the solution in this research. Then the probability of each cut sequence is calculated. Consequently, the probability of top event for each scenario is obtained. Finally, all scenarios are ranked based on top event probabilities. As a case study, optimization of sensor placement has been demonstrated on steam turbine and results are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Fuzzy-based system reliability of a labour-intensive manufacturing network with repair

This paper presents a fuzzy-based assessment model to evaluate system reliability of a labour-intensive manufacturing system with repair actions. Due to the uncertainty in human performance, labour-intensive manufacturing systems must determine the capacity of each labourer in order to accurately characterise the performance of the systems. Therefore, we model such a manufacturing system as a fuzzy multi-state network in order to characterise the labourers’ influence on workstation performance. First, the workstation reliability is defined according to the loading state by three fuzzy membership functions, namely ‘under loading’, ‘normal loading’ and ‘over loading’, respectively. The system reliability is subsequently evaluated with fuzzy intersection operations in terms of these workstation reliabilities. Thus, the system reliability is defined as a fuzzy membership function to assess whether the manufacturing system performance is sufficient to satisfy the demand reliably. A case study of a footwear manufacturing system is illustrated to explain the proposed model. Furthermore, we apply the proposed model to a non-labour-intensive manufacturing network in order to validate the applicability to this class of systems.     

New Noncontacting Inductive Analog Proximity and Inductive Linear Displacement Sensors for Industrial Automation

Noncontacting inductive sensors are applicable on a large scale for position detection or travel measurement in industrial applications. Reasons for such broad acceptance in many sectors of industry are noncontact and wear-free sensing of the target (any metal object), reliability and robustness, resistance to fouling, water tightness and compact size. The present work is intended to be a systematic, complete, and consistent presentation of the technological innovations, recent implementations and current trends regarding the analog distance and travel sensing offered by noncontacting inductive sensors for industrial applications. It starts with the fundamentals of inductive sensing and presents the physical basics gained by modern analytic and simulation methods, as well as high-level integrated circuits for inductive sensors. The following sections deal with present-day inductive analog proximity sensors and with the distinctive technological innovation offered by the new inductive linear displacement sensors and with miniaturization results achieved through consistent integration.     

Fuzzy Reliability Design for the Interference Articulation of a Roller Shaft

1 IntroductionA flour-milling machine is the most important piece of equipment for a mill, and the roller for the flour-milling machine is a key element. At present, most rollers are manufactured by using interference articulation between the roller and the shaft. As feed and distance between the rollers are of a certain randomness and fuzziness, the actual working torque of a roller is random and fuzzy. In addition, the interference quantities, modulus of elasticity and strength limits are al…     

考虑载荷作用次数的机械零部件可靠性灵敏度分析方法

 为了研究载荷多次作用时,机械零部件的可靠度及可靠性灵敏度变化规律,从灵敏度角度修改零件的设计参数,降低制造成本,建立一种可靠性模型结合了随机摄动法、Edgeworth级数技术,并考虑了载荷的作用次数．摄动法和Edgeworth级数可以在基本随机参数的前4阶矩已知的情况下,研究具有任意分布参数的机械零件的可靠性灵敏度设计问题,顺序统计量理论考虑了载荷作用次数在可靠度和灵敏度计算中的影响．使用这种模型计算出的可靠度会随着载荷作用次数而变化,这与静态的可靠度计算方法存在差别．以某一型号的螺栓为算例,应用此模型计算了其可靠度、随机变量均值和方差的可靠性灵敏度．由提出的方法得到了可靠度和可靠性灵敏度值及其随载荷作用次数变化的曲线．可靠度及可靠性灵敏度随载荷作用次数变化的规律是：载荷作用次数增加,可靠度值降低,变化趋势单调;载荷作用次数达到最大时,可靠度达到最小;随机变量均值和方差的灵敏度随载荷作用次数变化出现不同的变化趋势,其中螺栓截面直径的均值和方差灵敏度随载荷作用次数的变化最大,随作用次数的增加,螺栓截面直径的参数将对螺栓的可靠性起主要的决定作用．     

A study of operational and testing reliability in software reliability analysis

Software reliability is an important aspect of any complex equipment today. Software reliability is usually estimated based on reliability models such as nonhomogeneous Poisson process (NHPP) models. Software systems are improving in testing phase, while it normally does not change in operational phase. Depending on whether the reliability is to be predicted for testing phase or operation phase, different measure should be used. In this paper, two different reliability concepts, namely, the operational reliability and the testing reliability, are clarified and studied in detail. These concepts have been mixed up or even misused in some existing literature. Using different reliability concept will lead to different reliability values obtained and it will further lead to different reliability-based decisions made. The difference of the estimated reliabilities is studied and the effect on the optimal release time is investigated.     

A comparison of the simplified probabilistic method in R6 with the partial safety factor approach

Probabilistic assessments are a useful aid to decision making in areas such as safety analysis, design studies and the deployment of resources on maintenance, inspection and repair. In principle, a full probabilistic assessment requires a complete knowledge of the relevant failure models and the distributions for each of the input quantities. However, in practice, these requirements cannot normally be met in full and it is necessary to employ various simplifying assumptions and approximations in order to make the analysis tractable. The partial factor method and the simplified approach in R6 provide two relatively simple and independent methods of assessing failure probabilities using R6.The two methods have been applied to a set of test cases and the results compared. In the case of the partial safety factor method target reliabilities in the range 10⁻³–10⁻⁵ were considered. Sets of partial safety factors for load, defect size, fracture toughness and yield stress were taken from BS 7910 and used for assessments covering different regions on the R6 failure assessment diagram. A calculation of the assessed failure probability was also carried out for each of these sets of conditions using a simplified probabilistic approach developed for the R6 procedure.The assessed failure probabilities were compared with the corresponding target reliability assumed for the partial safety factor calculation. It was found that the partial safety factor assessments were generally conservative compared to the simplified approach. However, in many instances the assessed probabilities were several orders of magnitude smaller than the target reliabilities suggesting that the recommended values of partial safety factors in BS7910 were excessively conservative for some of these conditions.     

Reliability with finite buffer size for a multistate manufacturing system with parallel production lines

Abstract

This study examines system reliability for a manufacturing system with parallel production lines by creating a model of a multistate manufacturing system (MMS). System reliability is defined as the probability of demand satisfaction, which reflects the probability that the current capacity state of the MMS can successfully process a given demand. In particular, a buffer station with a finite size is taken into consideration to avoid blockage and starvation in the MMS. To the best of our knowledge, there is no existing research that considers a finite buffer size in a model of an MMS with parallel production lines. This study proceeds through the following phases. (i) In the model construction phase, the amount of input material, workload, and minimal capacity required by each workstation to satisfy a given demand level are studied by flow analysis; subsequently, a buffer usage matrix (BUM) is proposed to calculate buffer reliability. (ii) In the performance evaluation phase, system reliabilities with both infinite and finite buffer sizes are derived in terms of both minimal capacity vector and buffer reliability. (iii) In the case demonstration phase, two examples are utilized to illustrate the proposed method. Results of both examples show that the system reliability is overestimated with an infinite buffer size.