鱼雷配一次电池试验有关指标评定方法及样本量确定

鱼雷装载可靠度、实航工作可靠度、平均航速、航程等只与一次电池有关,本文研究了这些指标的试验评定方法,并根据结果评定要求,确定一次电池试验样本量。鉴于一次电池样本量有限的实际情况,为了降低研制单位风险,还提出了一次电池验前信息综合应用。     

仿真试验在水下武器装备试验与评价中的研究分析

针对仿真试验在水下武器装备试验鉴定中的角色和地位,详细分析了国外仿真试验技术的发展趋势,从验前信息融合、试验设计优化和作战效能评估三个方面深入探讨了仿真试验在水下武器装备试验评价中的综合应用,对于如何更好发挥仿真试验在水下武器装备试验与评价中的作用,具有一定的理论价值和参考意义。     

贝叶斯方法在船体结构疲劳分析中的应用

针对船体结构疲劳试验的小样本特点，探讨了确定船体结构疲劳寿命分布模型和建立疲劳性能曲线的贝叶新斯方法。该方法将先验信息与样本信息加以综合得到后验概率，将统计推断建立在后验概率的基础上，减小了因样本短而带来的统计分析误差，获得比传统方法更可靠的结果。     

确定船体结构疲劳寿命分布的Bayes方法

文中给出了由小样本试验数据确定确体结构疲劳寿命分布的Bayes方法,采用二级模糊综合评判获得先验概率,通过似然函数反映样本信息,利用Bayes定理综合先验信息与样本信息得到后验概率。将统计推断建立在后验概率的基础上,弥补了传统方法在统计推断上的缺陷。     

舰载武器控制系统可靠性评定研究

结合武器装备试验鉴定工作实际,给出了舰载武器武控系统可靠性鉴定试验常用的评定方法,并给出了不同评定方法的适用条件、应用原则,应用科学合理的评定方法,不仅对舰载武器武控系统可靠性作出客观公正地评价,还能在一定程度上节省试验时间,降低试验消耗。     

复杂系统可靠性验证方法研究磁

针对复杂系统可靠性验证问题,根据组成系统设备的研制特点,给出了复杂系统可靠性的Bayes综合评估方法,以此获得了其任务可靠度点估计和置信下限。在此基础上,对复杂系统可靠性验证试验方案的制定方法进行了研究,通过综合考虑生产方和使用方的风险,给出复杂系统可靠性保证试验方案的调整方法。最后,通过实例分析,对方法的可行性进行了说明。     

基于Bayes小子样理论的导弹贮存可靠性评估方法研究

针对导弹造昂贵价、样本少、贮存可靠性验证难度大的特点,本文探讨了基于小样本的Bayes统计分析模型,这种模型以故障率的共轭验前分布Gamma分布为先验分布,充分利用了历史试验数据的同时,结合导弹现场试验数据,给出了导弹故障率的后验分布,并推算出可靠性Bayes点估计和置信限。最后以某型导弹为例,利用其试验数据验证了此方法的有效性。     

基于Bayes拟合法的船舶复杂系统总体任务可靠度评估

针对现有系统可靠性评估方法不适用船舶复杂系统总体任务可靠度评估这一特点,提出了一种基于Bayes思想和矩估计理论的Bayes拟合方法.该方法从船舶系统总体任务可靠度特点出发,将无失效数据转化为等效失效数,由已知各种验前信息、历史信息和试验信息,求得系统总体任务可靠度后验分布函数,并给出不同置信度水平下系统总体任务可靠度下限,较好地解决了小子样、无失效、可维修情况下的船舶复杂系统总体任务可靠度评估问题.     

基于ADS-B数据的雷达标校新方法

在研究雷达试验评定方法的基础上,提出了应用ADS-B数据进行雷达试验的方法。从其评定的工作流程和试验结果来看,采用ADS-B导航信息的标校方法,大大简化了其评定的流程,避免了复杂的环节,拓展了ADS-B导航信息新的应用领域,提供了新的雷达标校方法。     

潜艇导航系统航向测量及精度评定方法研究

以某型潜艇导航精度试验为例,研究了一种潜艇导航系统航向测量及精度评定方法.评定方法及评定结果获得了潜艇总体设计单位、综合导航系统和武器系统研制单位及使用方的一致认可,并在随后进行的武器发射试验中得到验证,进而为准确分离发射潜艇对武器系统命中精度的影响提供了依据.     

某型号船用钢索在无失效情况下的可靠性研究

为研究船用特种钢索的可靠性,设计研制了相应的可靠性试验台,对5套钢索进行了截尾可靠性寿命试验.针对钢索试验中的无失效情况,确定了钢索失效概率的多层先验分布,并给出了其Bayes估计.用最小二乘法给出寿命分布的参数估计,进而得到可靠度估计.最终利用试验评估结果证明钢索满足可靠性要求.     

Bayes判别法在水下爆炸实验中的应用

介绍Bayes判别法的基本原理，使用该方法对装药在水下爆炸的模拟实验进行分析研究，并在理论和实际中进行了误判概率及爆炸效果的讨论和计算。该方法的有效性对实际应用具有一定的指导作用。     

An advanced risk analysis approach for container port safety evaluation

Risk analysis in seaports plays an increasingly important role in ensuring port operation reliability, maritime transportation safety and supply chain distribution resilience. However, the task is not straightforward given the challenges, including that port safety is affected by multiple factors related to design, installation, operation and maintenance and that traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic risk-based decision support in container terminals. The proposed approach can also be tailored for wider application in other engineering and management systems, especially when instant risk ranking is required by the stakeholders to measure, predict and improve their system safety and reliability performance.     

Integration of structural health monitoring in life-cycle performance assessment of ship structures under uncertainty

In this paper, an approach for integrating the data obtained from structural health monitoring (SHM) in the life-cycle performance assessment of ship structures under uncertainty is presented. Life-cycle performance of the ship structure is quantified in terms of the reliability with respect to first and ultimate failures and the system redundancy. The performance assessment of the structure is enhanced by incorporating prior design code-based knowledge and information obtained by SHM using Bayesian updating concepts. Advanced modeling techniques are used for the hull strength computations needed for the life-cycle performance analysis. SHM data obtained by testing a scaled model of a Joint High-speed Sealift Ship is used to update its life-cycle performance.     

贝叶斯树算法在异常入侵检测中的应用

研究了Windows平台下异常检测方法,提出了一种利用Windows Native API调用序列和基于贝叶斯树算法的主机服务进程规则与对应概率分布生成算法。根据长为N-1的Windows Native API调用序列预测第N个调用的概率分布,对生成的概率序列用U检验方法作为异常检测算法。以贝叶斯树作为弱分类算法,利用AdaBoost-M1方法构造多个基于贝叶斯树的概率分布序列,并按一定方式把它们组合成一个加强的概率分布序列进行入侵检测。实验结果表明这种方法能明显提高模型预测能力。     

Epistemic-based investigation of the probability of hazard scenarios using Bayesian network for the lifting operation of floating objects

Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method (SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types: the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.     

运用贝叶斯网络对浮台吊装进行基于知识的危险场景概率分析（英文）

Owing to the increase in unprecedented accidents with new root causes in almost all operational areas, the importance of risk management has dramatically risen. Risk assessment, one of the most significant aspects of risk management, has a substantial impact on the system-safety level of organizations, industries, and operations. If the causes of all kinds of failure and the interactions between them are considered, effective risk assessment can be highly accurate. A combination of traditional risk assessment approaches and modern scientific probability methods can help in realizing better quantitative risk assessment methods. Most researchers face the problem of minimal field data with respect to the probability and frequency of each failure. Because of this limitation in the availability of epistemic knowledge, it is important to conduct epistemic estimations by applying the Bayesian theory for identifying plausible outcomes. In this paper, we propose an algorithm and demonstrate its application in a case study for a light-weight lifting operation in the Persian Gulf of Iran. First, we identify potential accident scenarios and present them in an event tree format. Next, excluding human error, we use the event tree to roughly estimate the prior probability of other hazard-promoting factors using a minimal amount of field data. We then use the Success Likelihood Index Method(SLIM) to calculate the probability of human error. On the basis of the proposed event tree, we use the Bayesian network of the provided scenarios to compensate for the lack of data. Finally, we determine the resulting probability of each event based on its evidence in the epistemic estimation format by building on two Bayesian network types: the probability of hazard promotion factors and the Bayesian theory. The study results indicate that despite the lack of available information on the operation of floating objects, a satisfactory result can be achieved using epistemic data.     

导管架平台结构风险评估方法研究

文章提出了一种导管架平台在位风险评估方法,以惠州HZ32-2平台为例,首先基于倒塌分析法计算平台结构极限承载能力及构件重要度,并根据API规范中环境载荷的计算方法进行平台结构静力状态下的可靠性分析,最后建立基于风险的结构强度评估方案。计算结果表明本方法可为完善平台结构风险评估及优化平台检验计划提供重要参考。