考虑不完美维修的随机退化设备剩余寿命自适应预测方法

在现有考虑不完美维修的随机退化设备剩余寿命预测研究中,通常仅考虑维修活动对退化状态或退化速率的单一影响,仅有考虑二者双重影响的研究,忽略了退化设备的个体差异性。鉴于此,提出一种基于多阶段扩散过程的自适应剩余寿命预测方法,同时考虑不完美维修活动对设备退化状态和退化速率的影响,并利用随机游走模型描述退化速率随观测数据的更新过程以表征设备的个体差异性。基于历史退化数据,利用极大似然估计法得到退化模型参数的初值;基于状态观测数据,利用卡尔曼滤波算法和期望最大化算法自适应的更新模型参数。利用卷积算子和蒙特卡洛方法推导得到了首达时间意义下设备剩余寿命的概率密度函数。最后,通过仿真算例和陀螺仪的实例研究验证了所提方法的有效性和优越性。     

Sequential Monte Carlo Method Toward Online RUL Assessment with Applications

Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.     

Perturbation methods for the estimation of parameter variability in stochastic model updating

The problem of model updating in the presence of test-structure variability is addressed. Model updating equations are developed using the sensitivity method and presented in a stochastic form with terms that each consist of a deterministic part and a random variable. Two perturbation methods are then developed for the estimation of the first and second statistical moments of randomised updating parameters from measured variability in modal responses (e.g. natural frequencies and mode shapes). A particular aspect of the stochastic model updating problem is the requirement for large amounts of computing time, which may be reduced by making various assumptions and simplifications. It is shown that when the correlation between the updating parameters and the measurements is omitted, then the requirement to calculate the second-order sensitivities is no longer necessary, yet there is no significant deterioration in the estimated parameter distributions. Numerical simulations and a physical experiment are used to illustrate the stochastic model updating procedure.     

焦化加热炉炉管的红外热象检测与剩余寿命在线评估

利用红外热成象技术，在金属材料剩余寿命预测理论研究的基础上，研制一套在线剩余寿命评估系统，实现了焦化加热炉炉管方便而快捷的在线检测与评估。研究结果在某厂焦化加热炉的改造中得到成功应用，在运行状况下进行炉管表面温度场的红外热象测试和剩余寿命预测，可为厂方决策提供科学依据。     

电子产品动态损伤最优估计与寿命预测

针对电子产品寿命预测中存在的不确定性因素影响,提出一种基于粒子滤波的电子产品动态损伤最优估计和寿命预测方法.首先建立了电子产品动态损伤HMM模型;分析了电子产品动态损伤和寿命预测中的不确定性因素;通过贝叶斯滤波模型,将寿命预测的不确定性问题转化为最优估计问题;利用粒子滤波算法求解出电子产品动态损伤的最优估计值,从而进行寿命预测;实验证明,该方法可有效消除系统和测量因素的干扰,明显提高电子产品剩余寿命预测的精度.     

关键几何尺寸对转子叶片疲劳可靠性的影响

基于改进的一次二阶矩法,利用线弹性随机有限元分析和局部应力应变法,考虑几何尺寸随机性进行转子叶片结构疲劳寿命可靠性的数值分析.根据榫头喉部关键尺寸的随机特性确定应力的统计特性,然后采用一种等概率方法,通过确定性数值计算得到叶片寿命的概率密度、均值和变异系数,讨论榫头喉部几何尺寸的随机性对危险点寿命可靠度的影响.     

多产品采购条件下的供应商选择与订购量分配问题研究

为了解决随机性需求和价格折扣并存条件下的多产品采购供应商选择问题,建立了相应的多目标混合整数随机规划模型.该模型的特点是：①模型的约束条件中兼具确定性和随机性;②通过约束条件方程式准确地表现随机性需求和价格折扣两大假设条件.根据该模型的特殊结构,提出了一种适用的求解策略：首先,通过把机会约束转化为确定性等价类,而将多目标混合整数随机规划模型转化为多目标混合整数规划模型;然后,采用最大满意度法,将体现决策者偏好和目标模糊性的加权模糊多目标混合整数规划模型转化为求解等价的多个单目标混合整数规划问题;最后,在确定每个模糊目标的隶属度函数表达式的基础上求得问题的最优解.另外,通过应用算例说明了模型的有效性和可行性.     

Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method

According to the Taylor tool life equation, tool life reduces with increasing cutting speed following a power law. Additional factors can also be added, such as the feed rate, in Taylor-type models. Although these models are posed as deterministic equations, there is inherent uncertainty in the empirical constants and tool life is generally considered a stochastic process. In this work, Bayesian inference is applied to estimate model constants for both milling and turning operations while considering uncertainty.     

Tool life prediction using Bayesian updating. Part 2: Turning tool life using a Markov Chain Monte Carlo approach

According to the Taylor tool life equation, tool life reduces with increasing cutting speed following a power law. Additional factors can also be added, such as the feed rate, in Taylor-type models. Although these models are posed as deterministic equations, there is inherent uncertainty in the empirical constants and tool life is generally considered a stochastic process. In this work, Bayesian inference is applied to estimate model constants for both milling and turning operations while considering uncertainty.     

Diagnostic Monitoring of Rolling-Element Bearings by High-Frequency Resonance Technique

In the present work, investigations by high-frequency resonance technique for diagnosis of defect frequencies of rolling-element bearings are reported. Raw vibration signature of the bearings at different speeds of operation has been demodulated. Envelope detected spectrum is analyzed to evaluate various defect frequencies and their energy levels. Experimentally evaluated frequencies are compared with theoretically determined defect frequencies. These frequency values and their energy levels are used to monitor intrinsic condition of bearings as well as to establish severity of existing/developed defects in the bearings. Relative comparison of bearings of the same type are made at various operating speeds under identical conditions of operation on the basis of identified defect frequencies and severity of defects.

The paper gives a realistic approach to monitor intrinsic condition of a bearing. Investigations given in the paper may have a potential for performance evaluation and may act as a reliable tool to establish safe limit for bearing operation. Investigations may serve as a quality control instrument for the earliest detection of even the smallest nature of defect existing in a bearing and may be used for “on-line” bearing condition monitoring as well as routine “quality control” instrument on the test bed for the diagnostic monitoring of rolling-element bearings.     

基于随机有限元法的结构经济寿命分析

基于随机有限元,将材料和载荷等影响结构疲劳寿命的不确定因素视为随机变量,分别对结构的裂纹形成寿命和裂纹扩展寿命进行可靠性分析.将结构的疲劳寿命看成由裂纹形成寿命和裂纹扩展寿命组成的串联系统,并由此确定结构的检修周期.通过随机有限元得到的分析结果与Monte Carlo模拟结果的比较,表明此方法能给出合理的结构检修周期,具有较强的适用性.     

CONCEPT OF MODEL BASED TAMPERING FOR IMPROVING PROCESS PERFORMANCE: AN ILLUSTRATIVE APPLICATION TO TURNING PROCESS

This paper presents the concept of a methodology called Model Based Tampering (MBT), that considers the non-linear and stochastic nature of process dynamics, to compensate, in real-time, the effects of process degradation on the performance. The uniqueness of this concept emerges from the ability to combine and utilize the structures of the existing models to fit tractable and robust real-time control models in the form of low-dimensional nonlinear stochastic differential equations (n-SDEs). As an illustrative application of MBT, the minimization of diameter variation due to tool degradation in the turning process is considered. Through a series of simulation runs, the use of MBT was found to reduce diameter variation by 97%. The methodology is applicable to a wide range of manufacturing processes.     

Clutch wear prediction via a deterministic-stochastic modeling approach

The front surface temperature of a clutch pressure plate is studied for clutch wear prediction. A combined deterministic plus stochastic modeling approach is proposed to fit the front surface temperature data. The underlying clutch engagement process is explained by a deterministic function, whereas the inherent dynamics are explained by the stochastic model. The results of the combined modeling approach indicate that the front surface temperature can be modeled accurately to within ± 4°F.     

截尾试验下疲劳寿命分布的极值模型

针对截尾寿命试验数据缺少随机变量尾部分布和常规截尾数据分析对长寿命区高可靠度估计不准确的情况,根据极值统计学原理,给出一个结构元件疲劳寿命分布的极值模型.文中利用全样本试验数据构造截尾试验,采用传统的极大似然估计法给出寿命总体的分布形式,分析结构元件疲劳寿命的极值分布情况,建立一个新的寿命分布的极值模型,对长寿命区的寿命分布形式和高可靠度的估算有明显优势.三个算例表明,预测结果与全样本试验结果符合很好.     

Active magnetic bearings used as exciters for rolling element bearing outer race defect diagnosis

The active health monitoring of rotordynamic systems in the presence of bearing outer race defect is considered in this paper. The shaft is assumed to be supported by conventional mechanical bearings and an active magnetic bearing (AMB) is used in the mid of the shaft location as an exciter to apply electromagnetic force to the system. We investigate a nonlinear bearing-pedestal system model with the outer race defect under the electromagnetic force. The nonlinear differential equations are integrated using the fourth-order Runge–Kutta algorithm. The simulation and experimental results show that the characteristic signal of outer race incipient defect is significantly amplified under the electromagnetic force through the AMBs, which is helpful to improve the diagnosis accuracy of rolling element bearing׳s incipient outer race defect.     

柴油发动机寿命实时预测系统的设计与实现

通过三通道传感器设计实现柴油发动机缸压力、机油压力和喷油压力数据的在线同步采集,并导入柴油发动机寿命预测数学模型,实现不解体的情况下动态测算柴油发动机的剩余寿命,判断发动机工作状态及何时需要进行发动机检修.实验表明在同一地区车辆行驶里程越大,预测提前量也越大;西藏地区的总体预测提前量大于广西地区的预测提前量.     

一种基于空间卷积长短时记忆神经网络的轴承剩余寿命预测方法

传统的基于数据驱动的轴承剩余预测方法仍需要一定的先验知识,比如：特征指标选取、健康指标构建、失效阈值选定等等。预测结果严重依赖人工经验,为了克服这一缺点,基于深度学习方法提出了一种用于轴承剩余寿命预测的新方法,该方法的核心包括健康指标构建和剩余寿命计算。首先提出了一种无需先验知识的基于空间卷积长短时记忆神经网络（Convolutional long short-term memory neural network,ConvLSTM）的健康指标生成网络模型,该网络利用卷积神经网络的局部特征提取能力和长短时记忆网络的时间依赖特性,可直接从采集到的原始信号中挖掘反映退化程度的特征,构建健康指标,实现了高维原始数据向低维特征的映射转化,并利用Sigmoid函数将其归至[0,1]区间内,实现了阈值的统一;然后,利用粒子滤波更新双指数寿命模型,实现剩余寿命结果的输出。利用轴承全寿命试验对所提方法进行了验证,并与其他相关方法进行对比,结果表明本文方法所构建的健康指标具有更好的趋势性、单调性和鲁棒性,同时剩余寿命预测的准确率更高。     

具有混合数据的维修策略决策方法

设备维修策略决策问题的研究,不但可以降低维修成本,还可以提高设备的可用性、可靠性、安全性及产品质量。目前虽取得了一定的进展,但还从未解决混合数据下的维修策略决策问题。应用随机优势构造局部和谐指数和非和谐指数以解决维修策略决策中随机性评价下的比较问题;将模糊评价转化为模糊数,并利用模糊数质心距建立局部和谐指数和非和谐指数以解决模糊性评价下的择优问题。通过以上处理,在ELECTRE III方法中可以把维修策略决策中的随机性评价和模糊性评价视为确定性评价来对待,进而解决了设备维修策略决策中的混合数据问题。将其应用到火力发电系统的汽轮机中,试验结果表明,此方法是合理的和有效的。     

A New Statistical Model of Contact Fatigue

A detailed derivation of a new statistical model of contact fatigue life followed by its qualitative and quantitative analysis are presented. The model is based on contact and fracture mechanics and statistical treatment of the initial distribution of material defect. The model assumptions and their validation as well as the model properties are discussed. A parametric study of the model is performed. A generalization of the model for the case of stochastic residual stress or other contact parameters is proposed. Some analytical formulas for calculation of contact fatigue are proposed and analyzed. The validation of the model and its applicability to calculation of bearing fatigue life and some particular data are considered. A reflection of the quality of bearing manufacturing process on the contact fatigue model is discussed.     

Mechanical systems hazard estimation using condition monitoring

The ability to predict hazards of mechanical systems accurately can significantly enhance the predictive maintenance task. However, predicting hazards of systems accurately is non-trivial, especially when historical failure data are sparse or zero. The proposed proportional covariate model (PCM) overcomes this difficulty. This paper describes the concepts of PCM briefly and focuses on the estimation of the hazards of mechanical systems using accelerated life tests and condition monitoring data. This new approach to hazard estimation can reduce the number of accelerated life tests significantly. The hazard estimation can further be refined and updated with on-line condition monitoring data on a continual basis.