基于序贯验后加权检验的鱼雷抽样检验分析

Bayes理论是分析研究具有小子样特点鱼雷可靠性的有效方法,文中基于序贯验后加权检验的思想,给出针对二项分布的抽样检验基本方法,并将其应用在鱼雷系统抽样检验试验中.该方法充分利用抽样试验之前所能获得的先验信息,可有效减小试验所需的样本容量.最后以鱼雷系统为例进行分析,仿真结果验证该方法在减小试验样本容量的有效性,为鱼雷可靠性抽样检验试验提供一种新的分析方法.     

小子样产品可靠性一次抽样方法研究

根据抽样理论和小子样产品的可靠性特点,对小子样产品可靠性一次抽样的子样确定方法进行了修正,提出了更加合理的小子样产品可靠性一次抽样的子样确定方法,从而完善了有关小子样产品的一次抽样方案。结果表明,本文给出的抽样方案,不仅较好地反映批产品的质量水平,而且充分考虑了生产方和使用方的风险,具有一定的工程价值。     

基于Bootstrap方法的可靠性评估

针对机械系统可靠性试验数据分析中小子样疲劳性能数据难以满足经典统计方法容量要求的问题,将Bootstrap方法引入到小子样下母体百分位置信下限的计算。提出一种半参数Bootstrap方法用于计算正态分布变量的可靠度置信下限,并以某数控装备伺服驱动系统的可靠性分析实例验证该方法的可行性。     

航空发动机主轴轴承可靠性评估方法

航空发动机主轴轴承生产批量小且成本高，在进行可靠性试验时只能做小子样，短时间的定时截尾试验。针对这种情况就提出一种简单实用的小子样无失效试验的评估方法，并对 样无失效试验的可靠性抽样方案进行探讨。     

基于极小子样的某列车齿轮箱箱体疲劳寿命可靠性评估

针对采用Bootstrap方法对极小子样进行可靠性评估时,在重抽样的过程中样本向均值集中导致评估结果不够准确的问题,提出一种改进的Bootstrap可靠性评估方法。采用虚拟增广样本法对极小子样进行增广,增广至小样本。然后将增广后的小样本按照大小进行排序并分组,运用四分位差法和Bootstrap法对分组后的数据进行可靠性评估。最后,将该方法用于某列车齿轮箱箱体疲劳寿命的可靠性评估。评估结果表明:该方法可以较好地解决Bootstrap法无法对极小子样进行评估的问题,改善了Bootstrap法的抽样结果向均值集中的状况。     

基于Bootstrap与SVM集成的可靠性评估方法

针对传统的可靠性评估方法应用于数控装备时遇到的非线性与准确度低等问题,提出一种适用于小子样情况的数控装备可靠性评估新方法.综合采用Bootstrap和支持向量机等方法建立了小子样条件下的可靠性评估模型,给出了可靠性评估全过程的统一建模语言序列图,并进行了实例仿真分析.结果表明,该方法能够较好地解决小子样条件下可靠性评估遇到的非线性和评估准确度低等问题,并能够给出可靠性评估结果的可信性度量.     

小子样高速电主轴的Bayes可靠性分析

针对高速电主轴可靠性分析存在的问题,应用Bayes理论提出小子样高速电主轴性能退化可靠性试验分析方法,推导了小子样高速电主轴可靠性参数的验前、验后分布及置信限表达式,据此评定高速电主轴可靠性指标。对两根170MD18Y16型高速电主轴进行可靠性试验,利用性能退化量试验数据分析了Weibull寿命分布模型参数,并利用170MD18Y16型高速电主轴轴端跳动量测试数据,应用Bayes方法评定了该型高速电主轴可靠性指标,分析结果与电主轴实际工况使用值一致,表明Bayes理论可用于小子样高速电主轴可靠性分析。     

基于多源信息的复杂装备测试性评估

针对目前复杂装备测试性评估中的小子样问题,在对经典评估方法和传统Bayes评估方法进行分析的基础上,提出了一种基于多源信息融合的测试性评估方法。该方法综合考虑了多源信息的可靠度和可信度,较为合理地解决了多源信息对复杂装备测试性水平反映的可靠程度问题,以及融合求解后验分布时,因多源信息和现场试验信息的异总体特性而导致的小子样现场试验信息被淹没的问题。以某型火控系统测试性评估为例进行了研究,并与经典评估方法、传统Bayes评估方法进行了比较。结果表明,基于多源信息融合的测试性评估方法所得到的结果更为合理,在处理现场试验信息为小子样的问题时,更具优势。     

新型焊条自动计数检测系统

焊条在线快速准确自动计数问题一直是困扰我国焊条生产企业的一个难题,目前我国焊条生产企业普遍采用 单一计数方法,由于受环境干扰,计数不准确.针对这种一问题,采用接近开关计数和称重计数同时进行计数的综合计数法,开发出适合我国企业的焊条自动计数系统.由试验可知,该计数系统可快速、准确检测出焊条根数,运行可靠,有效提高焊条生产率.     

基于Bootstrap方法的小子样试验评估方法研究

Bootstrap方法是一种小子样试验评估方法。该方法在产生随机样本方面有不足之处,即产生的随机样本受到原始样本范围限制。因此本文在研究了用指数分布函数、Boltzm ann函数和三次多项式函数拟合修正样本经验分布函数的可行性之后,讨论用修正的样本经验分布函数替换传统Bootstrap方法中的经验分布函数,提出了基于Bootstrap方法的小子样试验评估方法。结论表明:采用Boltzm ann和三次多项式函数拟合修正样本经验分布函数后可使产生的随机样本不受原始样本范围限制,提高试验评估结果的精度,并附有算例说明所提出的方法。     

基于组态思想的旋转机械状态监测系统

为适应旋转机械状态监测系统多样化需求,提出一种新颖的基于组态思想的旋转机械状态监测系统。该系统以配置研究设计的组态化状态监测系统管理分析软件平台的通用计算机为核心,加上研制的电涡流信号采集前置分机、压电信号采集前置分机、工艺量信号采集前置分机和系统组合控制分机组成。系统可以依据工业现场状态监测的实际需要,由用户方便地组建成具有多种信号采集和数据分析功能的离线的便携式振动数据采集分析系统、准在线状态监测系统、或在线状态监测系统。所有信号采集前置分机和系统组合控制分机都是以ＤＳ８０Ｃ３２０高速处理单片机为核心设计的高速、小型化、低功耗系统。论述系统总体方案、系统分机设计和管理分析软件设计,并给出系统在工业现场的应用结果。     

基于压缩感知的非均匀脉冲SAR欺骗性干扰抑制方法

针对SAR欺骗式干扰的特点和传统干扰抑制后SAR成像质量恶化以及传统干扰抑制方法存在着冗余度高、有效信息提取率低等问题,本文提出了将两种新的稀疏采样方式-互质采样与嵌套式稀疏采样同压缩感知算法相结合,用来抑制欺骗式干扰并实现目标高分辨重建的方法。通过仿真实验,对比了本文提出的新重构算法与传统非均匀采样重构算法,证明了在采样率远低于Nyquist速率的条件下,基于压缩感知的稀疏非均匀SAR采样与成像方法不但可以抑制欺骗式干扰,而且能够在避免信号冗余的情况下实现目标的高分辨成像。     

便携式小冲杆试验微损取样装置的设计

小冲杆试验是一种对微型试样进行检测评价的方法,对在役设备的质量检测和剩余寿命预估具有重要作用。设计了一种应用于小冲杆试验的微损取样装置,并对取样的工艺路线进行了研究,主要包含安装定位、切割槽的加工、金刚石线锯切割以及刷漆修复等步骤。其中,砂轮机用于在设备表面加工切割槽,金刚石线锯切割机用于将微型试样取下。着重研究了金刚石线锯切割机中的张紧机构和摇摆机构,保证恒张力切割的同时提高了切割效率,充分发挥了线锯切割的优越性。这种取样装置体积小、重量轻,满足便携式的要求,能够适应多种环境下的微损取样。     

A design advisor for the optimal inspection of circularity tolerance

In the wake of growing importance for quality and the need to reduce inspection costs simultaneously, the need for a scientific method of selecting an optimum inspection strategy for coordinate measuring machine (CMM) based inspection has become very important. The inspection error resulting from CMM inspection is greatly affected by the profile irregularities and the sampling strategy, which includes sample size, sampling methods, and algorithms used for form evaluation. This paper describes a system that can recommend an optimal inspection plan based on the needs of the user. A design of experiments (DOE) based approach is used to relate the inspection error with sampling strategies. Surface irregularities are included in the form of lobes formed on the profile. A new two-way model is proposed that works in both directions between the sampling strategy and the performance metrics. The results indicate that the number of lobes and the sampling method used have little impact on the inspection error, while the sample size and form evaluation algorithms have a significant influence. An inspection plan advisor is presented, which provides an inspection plan based on the estimated shape and acceptable measurement error.     

Rectifying inspection for acceptable quality loss limit based on variable repetitive group sampling plan

It is usually assumed that a quality characteristic in an item obeys a normal distribution in the case that the quality of items is evaluated based on the variable property. Then, the concept of Taguchi’s quality loss has been accepted as the evaluation measure of quality instead of the traditional attribute property such as the proportion of nonconforming items. From this viewpoint, some variable sampling plans indexed by the quality loss have been investigated before now. As a study earliest among them, the variable single sampling plan based on operating characteristics (OC) indexed by the quality loss was considered. On the other hand, the attribute repetitive group sampling plan on OC was proposed for reducing the sampling number in the inspection. Recently, the variable repetitive group sampling (VRGS) plan on OC indexed by the quality loss has been considered. By the way, the rectifying inspection is known as one of the schemes of acceptance sampling inspection. Then, Dodge-Romig single sampling plans are known as the traditional rectifying inspection based on attribute sampling plans. Dodge-Romig rectifying attribute sampling plans provide the lot tolerance percent defective (LTPD) scheme on each lot and the average outgoing quality limit (AOQL) scheme for many lots. Furthermore, the rectifying variable single sampling (RVSS) plan indexed by the quality loss was investigated. In conformity with the traditional rectifying attribute sampling plans for the LTPD and AOQL schemes, the acceptance quality loss limit (AQLL) and specified permissible average outgoing surplus quality loss limit (PAOSQLL) schemes are respectively proposed in the RVSS plans indexed by the quality loss. In this article, we suppose that the quality characteristic in an item obeys a normal distribution. Under this condition, the rectifying variable repetitive group sampling (RVRGS) plan for AQLL is considered for the purpose of reducing the average total inspection (ATI). Specifically, the design procedure for finding out the required sample size and inspection criteria for satisfying the constraint of the quality assurance is derived. Lastly, it is shown that ATI of the RVRGS plan is reduced in comparison with that of the RVSS plan under the same condition.     

Adaptive sampling point planning for free-form surface inspection under multi-geometric constraints

Free-form surfaces have been widely used in aerospace, automotive and other fields. Due to its complex geometry, free-form surface inspection is generally conducted by touch-trigger or measuring probe-based Coordinate Measurement Machines or On-machine Measurement. Sampling strategy plays a decisive role in improving both measurement accuracy and efficiency, which is determined by sample size and distribution of sample points. However, it is difficult to simultaneously take the surface curvature, sampling density and approximation error into account, considering the complexity of surface geometry. In this paper, triangle mesh simplification is innovatively adopted in sampling planning to achieve multi-geometric constraints. As triangle mesh has outstanding advantages in representing the surface features, strong stability and is easy to modify its structure, free-form surface is converted to a dense triangle mesh. Triangle mesh simplification is implemented by iteratively contracting triangle edges. An improved quadric error metric is established to decide contraction order and optimal target vertices under discrete curvature constraint. Sampling density is controlled by limiting the triangle edge length. Detailed adaptive sampling algorithm under multi-geometric constraints is then developed. Both simulation and experiment are conducted to validate feasibility and robustness of the proposed method. The results are compared with uniform sampling and existing adaptive sampling strategy to show that the proposed method can prominently reduce sampling error when sample size is small.     

Bayesian Analysis on Deming’s Model with Consideration of Inspection Errors

This paper studies an extension to Deming’s model for inspection sampling. This new model is more useful in practice since it takes into account the two inspection errors (i.e. accepting out of tolerance components or rejecting in tolerance components) with the goal of minimising the expected total cost. Mathematical formulae corresponding to the model are derived based on the Bayesian approach. A computer program of this model is available for industry. Some numerical analysis of the effects due to model factors, such as inspection errors, component quality, and k₂ /k₁ ratio, is also presented. As a result, we conclude that a model that involves the two types of inspection error will lead to a looser inspection policy and higher expected total cost than a model without inspection errors. This method can be used to select the best suppliers when the total cost of the product is the main concern of the producer. ID="A1" Correspondence and offprint requests to: Dr C.-C. Chyu, Department of Industrial Engineering and Management, Yuan-Ze University, Chung-Li, Taiwan 320. E-mail: iehshsu@saturn.yzu.edu.tw     

航空发动机复杂磨损趋势的神经网络多变量预测模型

针对非等间隔的受加油因素影响的光谱油样分析数据的建模预测问题,建立了BP神经网络的多变量预测模型,充分考虑了油样分析数据的非等间隔性及受加油因素影响的特点,同时,用遗传算法对网络参数进行了优化。最后,利用两组实际的航空发动机油样光谱分析数据对模型进行了验证。结果表明,所提出的神经网络多变量预测模型能有效解决实际的受多因素影响的油样分析数据建模问题,具有较强的工程实用价值和通用性。     

Investigation on sampling size optimisation in gear tooth surface measurement using a CMM

Coordinate measuring machines (CMMs) are widely used in the gear manufacturing industry. One of the main issues for contact inspection using a CMM is the sampling technique. In this paper the gear tooth surfaces are expressed by series of parameters and inspection error compensation and initial value optimisation method are presented. The minimum number of measurement points for 3D tooth surfaces are derived. If high precision is required, more points need to be inspected. The sampling size optimisation is obtained from the criterion equation. The surface form deviation and initial values are optimised using the minimum zone method and genetic algorithms. A feature-based inspection system for spur/helical gears is developed and trials and simulations demonstrate that the method developed is suitable and very effective. This revised version was published online in October 2004 with a correction to the issue number.