基于斯坦克尔伯格博弈的订货提前期决策研究

研究了供应链中供应商和零售商两级间的订货提前期决策问题.考虑现实中常见而研究较少的供应商决定交货时间和零售商决定订货量的订货过程,在提前期管理成本由上下游分担的条件下,建立了基于斯坦克尔伯格博弈的订货提前期模型,对比分析了供应商先动和零售商先动情形下的提前期和订货批量决策,说明了先行公布提前期对降低供应商成本和缩短供应链中的提前期是有利的.最后,通过算例对模型和结论进行了验证.     

模具设计中塑件模型的处理

模具设计的主要依据是客户提供的塑件模型资料,如果所用软件不同,在导入塑件模型时,由于可能存在的几何缺陷会导致分模失败.因此在模具设计前,对塑件模型资料进行正确的处理非常重要,它直接关系到模具设计的成败,所以设计者应该熟练掌握.     

Application of relevance vector machine and logistic regression for machine degradation assessment

Degradation parameter or deviation parameter from normal to failure condition of machine part or system is needed as an object of prediction in prognostics method. This study proposes the combination between relevance vector machine (RVM) and logistic regression (LR) in order to assess the failure degradation and prediction from incipient failure until final failure occurred. LR is used to estimate failure degradation of bearing based on run-to-failure datasets and the results are then regarded as target vectors of failure probability. RVM is selected as intelligent system then trained by using run-to-failure bearing data and target vectors of failure probability estimated by LR. After the training process, RVM is employed to predict failure probability of individual units of machine component. The performance of the proposed method is validated by applying the system to predict failure time of individual bearing based on simulation and experimental data. The result shows the plausibility and effectiveness of the proposed method, which can be considered as the machine degradation assessment model.     

Integrated vendor–buyer cooperative inventory model with controllable lead time, ordering cost reduction, and service-level constraint

In this study, an integrated vendor–buyer inventory model is proposed. The vendor produces a product in a batch environment and delivers it to a buyer facing normally distributed demand. In the current study, several assumptions are used that the combination of these assumptions has not been studied in previous papers. Here is the list of these assumptions: service-level constraint instead of shortage cost is used; ordering cost reduction is allowed, and decreasing lead time by a cost dependent on two factors including shortened lead time and ordering quantity is considered. An algorithm is developed to obtain the optimal solution of the proposed model. A numerical example is included to illustrate the results of the model. Also, the effects of assumptions used in this paper are evaluated and discussed.     

Periodic inspection optimization model for a multi-component repairable system with failure interaction

This paper proposes a model to find optimal periodic inspection interval for a multi-component repairable system with failure interaction. Failure of one component of the system is hard, i.e., as soon as it occurs, the system stops operating. Failures of the other components are soft, namely, they do not cause the system stop. Soft failures increase the system operating costs and are detected only if inspection is performed. Thus, the components with soft failure are periodically inspected simultaneously and are repaired if found failed. When the component with hard failure fails, it is also repaired. However, it increases the failure rate of the other components. The objective is to find the optimal inspection interval such that, on a finite time horizon, the expected total cost is minimized. The proposed modeling approach is illustrated through a simplified numerical example for a general infusion pump.     

A multi-item ordering model in the (s, S ) inventory system

The decision variables in the (s,S) inventory system are s (ordering point) and S (maximum inventory level). In this paper, we propose a new ordering model in the (s,S) inventory system for the mixed part transportation problem with multi-item based on the depot system. The order range is introduced and used as decision parameters instead of the order point for ordering multi-item. Finally we test the proposed model with a numerical example and show computational results that verify the effectiveness of the model.     

Multivariate process parameter change identification by neural network

Whenever there is an out-of-control signal in process parameter control charts, maintenance engineers try to diagnose the cause near the time of the signal which does not always lead to prompt identification of the source(s) of the out-of-control condition, and this in some cases yields to extremely high monetary loses for the manufacturer owner. This paper applies multivariate exponentially weighted moving average (MEWMA) control charts and neural networks to make the signal identification more effective. The simulation of this procedure shows that this new control chart can be very effective in detecting the actual change point for all process dimension and all shift magnitudes considered. This methodology can be used in manufacturing and process industries to predict change points and expedite the search for failure causing parameters, resulting in improved quality at reduced overall cost. This research shows development of MEWMA by usage of neural network for identifying the step change-point and the variable responsible for the change in the process mean vector.     

Reliability evaluation of the servo turret with accurate failure data and interval censored data based on EM algorithm

The servo turret is a complex electromechanical hydraulic component that is the most likely to fail in a numerical control lathe. Reliability evaluation is used to make statistical inferences about the reliability characteristics of products according to all the information related to product reliability. Failure data is the basis of reliability evaluation; however, it is very difficult to collect many accurate failure data for reliability evaluation. In this paper, the reliability of servo turret is evaluated based on failure data that contains accurate failure data and interval censored data. First, a mixture Weibull distribution is chosen for fitting the reliability model. Then, expectation-maximization algorithm is used for estimating the parameters of the distribution which contains hidden variable, and the confidence interval of parameters is constructed using the delta method. In the simulation, different percentages of accurate data and interval data are used and compared with data containing only accurate data. The accuracy of this method is evaluated by mean square error. Finally, the method is applied to the failure data of servo turret and the parameters of mixture Weibull distribution are determined. For possibly simplifying the mixed Weibull distribution, the hypothesis of shape or scale parameters being equal is tested. The hazard property and mean time between failure are then estimated and associated 95 % confidence intervals are obtained.     

CHMM for tool condition monitoring and remaining useful life prediction

To solve the problems of tool condition monitoring and prediction of remaining useful life, a method based on the Continuous Hidden Markov Model (CHMM) is presented. With milling as the research object, cutting force is taken as the monitoring signal, analyzed by wavelet packet theory to reduce noise and extract the energy feature of the signal as a basis for diagnosis. Then, CHMM is used to diagnose tool wear state. Finally, a Gaussian regression model is proposed to predict the milling tool’s remaining useful life after the test sample data are verified to be consistent with the Gaussian distribution based on a reliable identification of the milling tool wear state. The probability models of tool remaining useful life prediction could be established for tools with different initial states. For example, when an unknown state of milling force signal is delivered to the milling tool online diagnostic system, the state and the existing time of this state could be predicted by the established prediction model, and then, the average remaining useful life from the present state to the tool failure state could be obtained by analyzing the transfer time between each state in the CHMM. Compared to the traditional probabilistic model, which requires a large amount of test samples, the experimental cost is effectively reduced by applying the proposed method. The results from the experiment indicate that CHMM for tool condition monitoring has high sensitivity, requires less training samples and time, and produces results quickly. The method using the Gaussian process to accurately predict remaining life has ample potential for application to real situations.     

正则化技术在状态空间载荷识别中的应用

针对载荷识别问题中的不适定性，提出了一种基于局部最小准则(local minimum criterion，简称LMC)的正则化技术处理方法。通过对线性系统离散状态空间方程的研究，建立了基于该正则化技术的状态空间载荷识别数学模型。仿真结果表明，该方法能有效地识别出载荷且计算效率较高，并结合数值试验结果分析研究了测点数、采样时间间隔和激励力频率因素对识别结果的影响，为载荷识别的高精度提供参考     

Minimizing costs while meeting safety requirements: Modeling deterministic (imperfect) staggered tests using standard Markov models for SIL calculations

In industry, potentially hazardous (technical) structures are equipped with safety systems in order to protect people, the environment, and assets from the consequences of accidents by reducing the probability of incidents occurring. Not only companies but also society will want to know what the effect of these safety measures is: society in terms of "likelihood of undesired events" and companies in addition in terms of "value for money," the expected benefits per dollar or euro invested that these systems provide. As a compromise between demands from society (the safer the better) and industry (but against what cost), in many countries government has decided to impose standards to industry with respect to safety requirements. These standards use the average probability of failure on demand as the main performance indicator for these systems, and require, for the societal reason given before, that this probability remain below a certain value depending on a given risk. The main factor commonly used in industry to "fine-tune" the average probability of failure on demand for a given system configuration in order to comply with these standards against financial risk for the company is "optimizing" the test strategy (interval, coverage, and procedure). In industry, meeting the criterion on the average probability of failure on demand is often demonstrated by using well accepted mathematical models such as Markov models from literature and adapting them for the actual situation. This paper shows the implications and potential pitfalls when using this commonly used practical approach for a situation where the test strategy is changed. Adapting an existing Markov model can lead to unexpected results, and this paper will demonstrate that a different model has to be developed. In addition, the authors propose an approach that can be applied in industry without suffering from the problems mentioned above.     

数控机床可靠性指标的似然比检验区间估计

为评估现场时间截尾少样本故障数据的多台数控机床的可靠性,基于似然比检验理论,提出包括初步粗略评估和最终精确评估的数控机床可靠性指标的似然比检验两步区间减半评估方法,推导计算2参数Weibull分布评估模型参数及平均无故障工作时间、可靠寿命和可靠度等可靠性指标区间估计的迭代公式。应用该方法初步评估时,可先取较大计算步长,寻找评估指标上凸曲线的最大值和下凹曲线的最小值,计算结果可取至整数,不需要精确计算。在第二步精确评估时,在最大(小)值左右两区间分别取减半区间或更小区间,细化步长,重新计算,直至找到满足精度的精确估计值。结合一具体实例,给出2参数Weibull分布评估模型参数及可靠性指标的点估计和区间估计。结果显示该方法可减少计算量,提高迭代速度,适用于少样本数据的可靠性分析。     

A model for creep life prediction of thin tube using strain energy density as a function of stress triaxiality under quasi-static loading employing elastic-creep & elastic-plastic-creep deformation

This paper demonstrates the application of a new multiaxial creep damage model developed by authors using stress traixiality to predict the failure time of a component made of 0.5%Cr-0.5%Mo-0.25%V low alloy steel. The model employs strain energy density and assumes that the uniaxial strain energy density of a component can be easily calculated and can be converted to multi-axial strain energy density by multiplying it to a function of stress trixiality which is a ratio of mean stress to equivalent stress. For comparison, an elastic-creep and elastic-plastic-creep finite element analysis (FEA) is performed to get multi-axial strain energy density of the component which is compared with the calculated strain energy density for both cases. The verification and application of the model are demonstrated by applying it to thin tube for which the experimental data are available. The predicted failure times by the model are compared with the experimental results. The results show that the proposed model is capable of predicting failure times of the component made of the above-mentioned material with an accuracy of 4.0%.     

基于故障风险的状态维修检测间隔期的确定

针对机械产品故障率随工作时间增加而增大的实际情况,提出了故障风险的概念。基于等故障风险,建立了新产品状态维修变间隔期检测模型,采用役龄回退因子描述维修后故障率降低的程度,建立了维修后产品状态维修检测间隔期模型,并进行了算例分析。所提出的方法可用于具有安全性故障后果或任务性故障后果的机械产品状态维修检测间隔期的确定。     

Roller bearing safety region estimation and state identification based on LMD–PCA–LSSVM

The basic idea of safety region is introduced into roller bearing condition monitoring. Local mean decomposition (LMD), principal component analysis (PCA) and least square support vector machine (LSSVM) are used comprehensively for the estimation of the safety region and the identification of normal state and faulty state for the roller bearing operational status. First, the vibration acceleration data was segmented according to a certain time interval and then Product Functions (PFs) of each piece of the data were obtained by LMD. Based on this, statistics control limits T² and SPE were extracted by PCA as roller bearings’ state characteristics. Finally, LSSVM was used for the estimation of the safety region of the roller bearing operation state, and multi-class LSSVM was used for the identification of the four normal, ball fault, inner race fault and outer race fault states. The results show that both the safety region estimation and state identification are accurate, and confirm the validity of the LMD–PCA–LSSVM method.     

基于可靠性的钢丝绳更换决策

为了提高钢丝绳使用寿命,保证其安全运行,论文运用weibull分布模型,建立累积故障与运行时间之间的关系,提出基于可靠性的更换决策。重点描述了依据当前运行时间及状态预测条件剩余寿命和确定下步维修间隔,并利用算例进行分析。计算结果表明,该更换决策具有可行性。     

累积冲击作用下可恢复退化过程的可靠度评估

以对激光器进行可靠度评估为背景,在考虑累积冲击与自然退化同时引起产品失效的基础上,重点研究了冲击时间间隔对产品退化量的影响,建立了累积冲击作用下可恢复的退化模型。根据自然退化过程及泊松冲击更新理论,研究了累积冲击模型和累积冲击下的退化失效模型。引入条件分布,分析冲击时间间隔对退化量的影响,得出了累积泊松冲击下的可恢复退化失效模型。结合性能退化分析,给出了可恢复的退化过程的失效分布函数和可靠度函数。进行了激光器退化试验,结果表明:当激光器工作2 000h时,利用累积冲击下可恢复的退化失效模型评估得到的可靠度为0.53,与仿真结果相符,为利用累积冲击下退化失效模型评估得到的可靠度的2.79倍。结果验证了本文提出的利用可恢复的退化模型进行可靠度评估的合理性。     

Optimal production policy for a manufacturing system with volume flexibility in a supply chain under lumpy demand

A batch production system with volume flexibility in a supply chain where a manufacturer procures raw materials and/or component parts from multiple suppliers in a lot and processes them into a single product, is considered in this research. First of all, a decision-making model is developed for an optimal production rate selection in the volume-flexibility production system, where a single type of finished products is delivered periodically in fixed quantities within a fixed interval of time. In this model, the production rate is perceived as a decision variable and unit production cost becomes a function of production rate. This model takes into account not only inventory costs of raw materials and finished goods, but production costs as well. Then, a pragmatic computation approach is presented to solve the proposed model for special unit-production-cost functions. It is used to determine an optimal production rate, and thus to decide an optimal ordering policy for procurement of raw materials from suppliers and an optimal manufacturing batch size. Some observations are made for optimal production rate analysis. Finally, a numerical study is conducted to illustrate the solution and a set of data with ten problems is tested and compared.     

基于可靠性等劣化理论的设备寿命周期预估

为了建立以可靠性为中心维修思想下的机械设备维修体制,在已有可靠性性能劣化模型的基础上,分析了大修周期与可靠性的关系,结合产品使用性能要求,提出一种新的可靠性阶段划分的方法,以科学确定机器的大修周期和报废指标,预估设备功能故障状态出现时间,进行机械设备寿命预测。并在此基础上改进RCM分析技术,用来评价故障结果,应用逻辑决断图选择预防性维修工作类型,最后形成更为有效的预防性维修大纲。与传统的寿命预测方法相比,这种方法对实验数据的依赖性低于传统方法。     

Maintenance scheduling to support the operation of manufacturing and production assets

Manufacturing and production plants operate physical assets that deteriorate with usage and time, thus, maintenance actions are required to restore the assets back to their original predetermined operational conditions. In this paper, we extend previous work on maintenance scheduling to considering a multi-component system that optimises both cost and reliability simultaneously. The model uses the concept of imperfect maintenance and includes factors such as ageing due to the operation rate of the system, downtime for maintenance and lead time for spare parts. For each maintenance planning period, the model predicts which of the three possible actions (namely, maintaining the component, replacing the component or doing nothing) for each component should be taken, such that the reliability is at least at a required level and the net present cost for the entire planning period is minimised. The entire approach is illustrated through the use of a numerical example and the evaluation of the model is done by using an evolutionary algorithm.