融合应力集中因子与贝叶斯理论的结构健康评估方法

针对传统结构健康评估方法评估精度较低的现状,提出一种融合应力集中因子与贝叶斯理论的结构健康评估方法。首先,结合基于应力集中因子的序化模型策略与非齐次泊松过程,运用贝叶斯理论方法对试验信息及总体过程参数的序化关系进行有效融合,推导出适合不同初始损伤状态的过程参数先验分布。同时,基于应力集中递进因子与似然函数的概念,建立融合应力集中因子与贝叶斯定理非齐次泊松过程的参数后验分布预测方法。最后对提出的结构健康评估方法进行实例研究,并将预测结果与独立的试验结果进行比较。结果表明,提出方法的平均预测精度为91.99%,可以实现通过少量试验信息完成结构健康状态评估的目的,从而为结构健康的精确评估提供了一种思路。     

A coupled dynamics,multiple degree of freedom process damping model,Part 2: Milling

Self-excited vibration, or chatter, is an important consideration in machining operations due to its direct influence on part quality, tool life, and machining cost. At low machining speeds, a phenomenon referred to as process damping enables stable cutting at higher depths of cut than predicted with traditional analytical models. This paper describes an analytical stability model for milling operations which includes a process damping force that depends on the surface normal velocity, depth of cut, cutting speed, and an empirical process damping coefficient. Model validation is completed using time domain simulation and milling experiments. The results indicate that the multiple degree of freedom model is able to predict the stability boundary using a single process damping coefficient.     

TOOL LIFE PREDICTION USING RANDOM WALK BAYESIAN UPDATING

According to the Taylor tool life equation, tool life reduces with increasing cutting speed. The influence of additional factors can also be incorporated. However, tool wear is generally considered a stochastic process with uncertainty in the model constants. In this work, Bayesian inference is applied to predict tool life for milling/turning operations using the random walk/surface methods. For milling, Bayesian inference using a random walk approach is applied to the well-known Taylor tool life model. Tool wear tests are performed using an uncoated carbide tool and AISI 1018 steel work material. Test results are used to update the probability distribution of tool life. The updated beliefs are then applied to predict tool life using a probability distribution. For turning, both cutting speed and feed are considered. Bayesian updating is performed using the random surface technique. Turning tests are completed using a coated carbide tool and forged AISI 4137 chrome alloy steel. The test results are applied to update the probability distribution of tool life and the updated beliefs are used to predict tool life. While this work uses the Taylor model, by following the procedures described here, the technique can be applied to other tool life models as well.     

机械系统的Bayes可靠性评估

讨论了机械系统的Ｂａｙｅｓ可靠性评估问题,当应力、强度均服从正态分布,且分布参数未知的情况下,首先分析了机械产品单元可靠性评估问题,讨论了给定验前分布情况下的单元可靠性Ｂａｙｅｓ精确下限,给出了无验前信息时的可靠性Ｂａｙｅｓ精确下限,鉴于精确限计算的复杂性,给出了应力－强度模型可靠性下限的近似计算方法,同时给出了可靠性的近似分布,然后把机械单元的可靠性信息折合到机械系统上,利用最大熵方法得到了系统可靠的验前分布,若没有做系统的可靠性试验,则根据此验前分布就可以对机械系统的可行性进行评估;若做了系统级可靠性试验,则利用Ｂａｙｅｓ公式求得系统可靠的验后分布,然后利用验后分布进行可靠评估,仿真实例说明机械系统Ｂａｙｅｓ可靠性评估的应用。     

Model updating of rotors supported on ball bearings and its application in response prediction and balancing

This work attempts experimental studies in finite element model updating of an actual rotor system mounted on ball bearings by using Inverse Eigen Sensitivity Method (IESM). The IESM is applied on state space representation of equations of motion and is used to identify bearing stiffness, damping and shaft material damping parameters. Non-proportional viscous damping model is used to model the bearing and shaft material damping. The experimental identification of viscous coefficient of shaft material damping was not found in the available literature and this work attempts the same as well. The updated model is validated for its accuracy by comparing the predicted frequency response with that obtained from the experiments. Finally, it is shown that the updated finite element model of the rotor system can be efficiently used to predict the unbalance in the rotor.     

Bootstrap法在车身质量评定中的应用

引入Bootstrap重采样技术，根据少量历史数据获得合格率分布的非参数化数值解，作为先验条件与当前样本分布相结合，通过贝叶斯方法对尺寸质量参数进行估计。避免传统的参数化先验分布所带来的人为假设，提出的方法更具有客观性，统计仿真验证该方法具有良好的精度。     

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.     

数控机床时变切削振动自适应稳定控制方法

针对数控机床切削过程中因不确定因素多,导致刀尖运动刚度和阻尼力难以保持恒定而造成的强振动问题,提出一种基于模态稳定的自适应稳定控制方法。模拟机床切削的连贯加工过程,获得影响最大的阻尼力和刚度系数;将其作为模糊动态振动表达模型的初始输入数值,通过响应函数得到二者在时变振动状态下的分布可能结果;利用置信函数得到阻尼和刚度系数的安全置信区间,以该区间作为判定依据,对超过区间的系数实行模态控制,完成切削振动的稳定控制。从切削振动的位移信号和声音信号 2 个方面进行仿真验,结果表明,该方法具有一定的可靠性和稳定性,可保证刚度和阻尼系数的状态恒定。     

Fault detection in roller bearing operating at low speed and varying loads using Bayesian robust new hidden Markov model

This paper uses Bayesian robust new hidden Markov modeling (BRNHMM) for bearing fault detection and diagnosis based on its acoustic emission signal. A variational Bayesian approach is used that simultaneously approximates the distribution over the hidden states and parameters with simpler distribution hence using Bayesian inference for the estimation of the posterior HMM hyperparameters. This allows for online detection as small data sets can be used. Also, the Kullback-Leibler (KL) divergence is effectively used to access the divergence of the probability function of the BRNHMM, to find its lower bound approximation and by applying a linear transform to the maximum output probability parameter generation (MOPPG). The training set result obtained from BRNHMM is then compared to the result from artificial neural network (ANN) fault detection for same complex system of low speed and varying load conditions which are difficult from a diagnostic perspective, as found in rolling mills.     

A new damping modelling approach and its application in thin wall machining

In this paper, a new approach to modelling the damping parameters and its application in thin wall machining is presented. The approach to predicting the damping parameters proposed in this paper eliminates the need for experiments otherwise used to acquire these parameters. The damping model proposed was compared with available damping models and experimental results. A finite element analysis and Fourier transform approach has been used to obtain frequency response function (FRF) needed for stability lobes prediction. Several predicted stable regions using both experimental and numerical FRF’s for various examples gave a good comparison.     

A boundary element model for acoustic responses in the ear canal and its statistical validation and updating

A boundary element method (BEM) model for acoustic responses in individual ear canals was developed, and its validity was assessed and updated using a statistical approach. The BEM model was developed using high resolution computed tomography (CT) scanning from a healthy male adult. The BEM model, which was discretized adaptively for a frequency band of up to 22 kHz, included the head, pinna, ear canals, and tympanic membranes. The variability of the BEM model due to the uncertain acoustic boundary conditions and measurement locations was predicted using the eigenvector dimension reduction (EDR) method. Then, the likelihood function estimation approach was introduced in order to measure the agreement between the acoustic responses of the simulation model and experimental results. In order to enhance the simulation model performance, the acoustic boundary conditions of the BEM model were updated using a statistical calibration approach that maximizes the likelihood function value between the calculated probability density functions (PDFs) of the simulation model and the measurement data. The results of the validation and calibration procedures applied to the BEM model demonstrated that the proposed method provides a very effective method of verifying the model validity and enhancing the performance of the simulation model.     

装备测试性增长过程的贝叶斯验证方法

现有测试性验证试验方案需要较多故障样本,存在测试性验证时间长、结果置信度低的问题,为此提出一种装备测试性增长模型的贝叶斯验证方法,在小子样、异总体的情况下进行测试性验证。建立装备测试性水平动态增长的数学模型,依据已有的研制阶段的数据进行求解,得到测试性增长模型的具体参数值,并对测试性水平进行预测。确定测试性水平的先验分布,使用最大熵法求解分布参数,采用贝叶斯理论融合现场试验数据得到后验分布,实现对装备测试性水平的验证。实例分析表明,该方法在较少样本量的情况下能够提高验证结果的置信度,降低双方风险。     

密封和内阻尼对火箭发动机液氢涡轮泵转子系统动力稳定性的影响

考虑内阻尼的Timoshenko梁一轴有限元离散化转轴,用Muszynska密封力模型描述非线性密封流体激振力,建立实际火箭发动机液氢涡轮泵转子系统的非线性有限元模型。结合已有试验数据,结合打靶法和Floquet 理论研究该系统在刚性支承和柔性支承两种工况下密封和内阻尼对系统稳定性的影响。分析轴承阻尼、轴向雷诺数以及密封各参数在两种支承下对系统临界失稳角速度的影响。     

基于贝叶斯自适应估计的光子计数集成成像

针对光子数极少环境下三维目标的重构问题,基于光子计数集成成像系统提出了一种贝叶斯自适应估计方法,来提高三维目标深度切片的重构质量。首先,通过光子计数集成成像系统获得一系列光子计数元素图像。接着,从光子计数过程的泊松分布出发,利用集成成像系统中对于同一个目标像素的多次采样特性,引入了局部自适应均值因子,从而建立起元素图像像素光子数估计的单参数后验概率模型。最后,通过后验概率模型的均值计算获得更新后的光子计数元素图像,并基于光束可逆原理重构出深度切片图像。实验结果表明:采用该方法在场景的两个深度处重构的切片图像相比传统贝叶斯重构图像的峰值信噪比提高了7.4dB和8.5dB,极大地提升了微弱光三维目标的重构质量。     

A heuristic threshold policy for fault detection and diagnosis in multivariate statistical quality control environments

In this paper, a heuristic threshold policy is developed to detect and classify the states of a multivariate quality control system. In this approach, a probability measure called belief is first assigned to the quality characteristics and then the posterior belief of out-of-control characteristics is updated by taking new observations and using a Bayesian rule. If the posterior belief is more than a decision threshold, called minimum acceptable belief determined using a heuristic threshold policy, then the corresponding quality characteristic is classified out-of-control. Besides using a different approach, the main difference between the current research and previous works is that the current work develops a novel heuristic threshold policy, in which in order to save sampling cost and time or when these factors are constrained, the number of the data gathering stages is assumed limited. A numerical example along with some simulation experiments is given at the end to demonstrate the application of the proposed methodology and to evaluate its performances in different scenarios of mean shifts.     

MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control

Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.     

Data mining and statistical inference in selective laser melting

Selective laser melting (SLM) is an additive manufacturing process that builds a complex three-dimensional part, layer-by-layer, using a laser beam to fuse fine metal powder together. The design freedom afforded by SLM comes associated with complexity. As the physical phenomena occur over a broad range of length and time scales, the computational cost of modeling the process is high. At the same time, the large number of parameters that control the quality of a part make experiments expensive. In this paper, we describe ways in which we can use data mining and statistical inference techniques to intelligently combine simulations and experiments to build parts with desired properties. We start with a brief summary of prior work in finding process parameters for high-density parts. We then expand on this work to show how we can improve the approach by using feature selection techniques to identify important variables, data-driven surrogate models to reduce computational costs, improved sampling techniques to cover the design space adequately, and uncertainty analysis for statistical inference. Our results indicate that techniques from data mining and statistics can complement those from physical modeling to provide greater insight into complex processes such as selective laser melting.     

基于DGM(1,1)的成败型系统可靠性增长Bayes评价模型

针对有多个研制阶段的成败型系统,提出了一种可靠性增长的Bayes评定模型。该模型首先根据Laplace检验,确定系统的可靠性是否增长;然后根据各个阶段可靠度的点估计,运用DGM(1,1)模型对现场试验的可靠度进行预测,得到验前信息,再运用列联表分析计算验前信息与现场试验信息的相似程度;最后,结合Bayes方法对验前信息和现场信息进行融合,在给定的置信度下,得到系统的可靠度置信下限。实例分析表明了该模型的有效性。     

A new method to determine the number of experimental data using statistical modeling methods

For analyzing the statistical performance of physical systems, statistical characteristics of physical parameters such as material properties need to be estimated by collecting experimental data. For accurate statistical modeling, many such experiments may be required, but data are usually quite limited owing to the cost and time constraints of experiments. In this study, a new method for determining a reasonable number of experimental data is proposed using an area metric, after obtaining statistical models using the information on the underlying distribution, the Sequential statistical modeling (SSM) approach, and the Kernel density estimation (KDE) approach. The area metric is used as a convergence criterion to determine the necessary and sufficient number of experimental data to be acquired. The proposed method is validated in simulations, using different statistical modeling methods, different true models, and different convergence criteria. An example data set with 29 data describing the fatigue strength coefficient of SAE 950X is used for demonstrating the performance of the obtained statistical models that use a pre-determined number of experimental data in predicting the probability of failure for a target fatigue life.

     

克里金模型及其在全局优化设计中的应用

介绍了克里金模型的基本理论,并采用以克里金模型为近似模型的贝叶斯分析算法,对函数关系复杂、难以计算情况下的全局优化问题进行求解.该算法使用实验设计的方法选取初始样本点,通过样点填充准则找到新的样本点来更新克里金模型,不断循环,从而找到全局最优点.通过实例计算证明,与传统优化算法相比,此方法计算速度快、精度良好.