考虑不完全维护影响的随机退化设备剩余寿命预测

针对寿命周期中存在不完全维护影响的随机退化设备剩余寿命难以预测的问题,提出了一种考虑不完全维护影响的退化建模和剩余寿命预测方法.首先,在Wiener过程理论架下,建立了能够表征不完全维护影响的分阶段退化过程模型,然后从阶段时间服从的逆高斯分布出发,利用逆高斯分布的卷积特性,从理论上推导出存在不完全维护下寿命分布的解析解,并将维护效果的随机性和维护次数的影响传递到寿命分布中.进一步通过时间尺度变换,得到了考虑未来存在不完全维护影响下的剩余寿命分布解析解.通过极大似然估计和最小二乘法对模型未知参数进行了估计.最后将本文方法应用到陀螺仪的实际退化过程中,验证了所提方法的有效性.     

运行状态切换下的设备剩余寿命预测

在工程实际中,很多系统在其寿命周期中都存在贮存和工作状态的切换,且运行状态的切换对设备的退化过程有较大影响,而当前缺少相应的研究。鉴于此,本文系统考虑了运行状态切换和状态持续时间的随机性以及不同状态有不同退化率等关键问题,利用连续时间马尔可夫链(CTMC)建立了刻画贮存-工作状态切换的系统运行模型,得到运行状态切换时间、切换次数、状态持续时间的联合概率分布,并将其融入随机系数回归模型,实现退化系统的剩余寿命估计。仿真试验表明,所提方法能有效实现运行状态切换下的系统剩余寿命估计。     

基于随机退化数据建模的设备剩余寿命自适应预测方法

针对现有剩余寿命预测研究中需要多个同类设备历史数据离线估计模型参数的问题,本文提出了一种基于退化数据建模的服役设备剩余寿命自适应预测方法.该方法,利用指数随机退化模型来建模设备的退化过程,基于退化监测数据运用Bayesian方法更新模型的随机参数,进而得到剩余寿命的概率分布函数及点估计.区别于现有方法,本文方法基于设备到当前时刻的监测数据,利用期望最大化算法对模型中的非随机未知参数进行在线估计,由此无需多个同类设备历史数据.最后,通过数值仿真与实例分析,验证了本文方法在剩余寿命预测时的有效性.     

金属化膜脉冲电容器剩余寿命预测方法研究

 金属化膜脉冲电容器是惯性约束聚变激光装置的重要元器件之一,其寿命预测是激光装置维护和备件决策制定的依据.在分析金属化膜脉冲电容器退化失效机理的基础上,采用Wiener过程描述其性能退化过程.进一步考虑到各电容器之间的差异,将Wiener过程的漂移参数和扩散参数看成随机变量,提出了随机效果Wiener过程模型,由同一批电容器的历史性能退化数据拟合其分布.在对单个电容器进行寿命预测时,采用Bayes方法融合电容器总体信息与该电容器自身的性能退化信息,得到其剩余寿命参数的验后估计,因而在电容器性能退化数据较少时采用该方法能提高剩余寿命预测精度.     

基于EM-EKF与隐含比例退化模型的机载电子设备剩余寿命自适应预测

针对现有机载电子设备剩余寿命自适应预测方法在新研小样本条件下,未能综合考虑设备隐含退化建模与漂移系数在线更新的问题,本文提出一种基于期望最大-扩展卡尔曼滤波(Expectation Maximization-Extended Kal-man Filter,EM-EKF)与隐含比例退化模型的机载电子设备剩余寿命自适应预测方法.首先,基于非线性Wiener过程构建带比例关系的设备隐含退化模型;其次,在引入漂移系数更新机制的基础上建立设备退化状态方程,并采用EKF算法同步更新设备退化状态与漂移系数;然后,采用EM-EKF算法实现对退化模型参数的自适应估计;最后,基于全概率公式,推导出设备剩余寿命的概率密度函数.通过对单台微机械陀螺仪实测数据进行分析,验证了本文所提方法具有更好的模型拟合性与预测准确性.     

融合非线性加速退化模型与失效率模型的产品寿命预测方法

针对高可靠性产品寿命数据少、获取成本高的问题,基于充分利用产品在研制、加速试验等不同环境下的退化数据、失效数据等可靠性数据的思想,提出了一种融合非线性加速退化模型和失效率模型的产品寿命预测方法.首先,根据退化数据对非线性退化过程进行分析,估计退化过程的参数;然后,根据加速退化数据及相应的加速退化模型估计加速退化模型的参数,从而得到退化参数与应力之间的关系.进一步,利用比例风险模型融合产品的寿命数据和未失效截尾数据,并基于此计算产品的可靠度函数、预测产品的寿命.实例应用验证了所提方法的有效性,同时说明了所提方法的应用价值.     

融合随机退化过程与失效率建模的设备剩余寿命预测方法

剩余寿命预测对于设备的维修与保养具有十分重要的意义。现有的剩余寿命预测方法大多只利用了设备的当前退化信息,对设备的历史寿命信息没有充分利用,而这些信息往往包含着设备寿命的演化信息,对于准确预测设备的剩余寿命具有重要意义。针对这个问题,提出了一种融合随机退化过程与失效率建模的设备剩余寿命预测方法。该方法首先将设备的退化过程建模为Wiener过程,然后利用Cox比例失效模型建模的方法融合设备退化过程对设备失效率的影响,由此达到利用设备历史监测信息的目的。进一步通过Bayes方法,利用当前退化监测信息对退化过程模型的参数进行更新,基于此进行剩余寿命预测,从而实现设备历史数据与当前数据的有效融合。最后,通过激光发生器的退化测量数据验证了提出的方法,说明该方法是有效的,具有一定的应用价值。     

基于相似性的剩余寿命预测：鲁棒性与不确定性研究

基于相似性的剩余寿命预测方法是近年来兴起的一类部件寿命预测方法。关于该方法预测结果的鲁棒性及不确定性研究尚未见报道,然而上述性质对于广泛应用该方法具有重要意义。首先,介绍了基于相似性的剩余寿命预测方法的主要思想,并介绍了一种基于相似性的剩余寿命预测方法（简称方法A）,提出一种基于历史样本估计来预测不确定性的方法;而后,基于一个广泛应用的随机衰退模型,在比较方法A的预测结果与某基于时间序列预测的剩余寿命预测方法结果的过程中,探究了方法A预测结果的鲁棒性;最后,基于同样的数据,运用所建议的方法,考察了方法A预测结果的不确定性。     

多源数据融合建模的寿命预测方法

针对单一传感器在设备状态监测期间不能很好地进行退化建模和剩余使用寿命预测的问题,提出了一种多源数据融合建模的寿命预测方法.首先,根据设备的退化性能,构造了复合健康指标;其次,使用非线性漂移维纳过程对设备进行退化建模,通过使用极大似然法估计模型参数后,推导出设备剩余寿命概率密度函数;最后,对所提出的方法进行了验证,并与单一传感器预测结果进行了对比,结果表明此方法具有较高的准确性.     

基于维纳过程的卫星用光纤陀螺剩余寿命预测

卫星用光纤陀螺的剩余寿命预测是卫星健康状态管理中的一个关键问题。针对传统的退化过程建模不能考虑同批设备中个体差异的问题,提出采用一种基于维纳过程的随机变量模型对光纤陀螺在空间环境下的退化特性进行建模。该模型将维纳过程中的漂移系数看成随机变量以描述个体差异,传统的维纳过程是其特例。依据该模型,可以得到光纤陀螺的可靠性指标和剩余寿命信息。仿真试验表明,文中提出的退化建模方法的精度明显高于传统方法,具有一定的工程应用价值。     

Prognostics of Lithium-ion batteries based on state space modeling with heterogeneous noise variances

Prognostics and health management of lithium-ion batteries, especially their remaining useful life (RUL) prediction, has attracted much attention in recent years because accurate battery RUL prediction is beneficial to ensuring high reliability of lithium-ion batteries for providing power sources for many electronic products. In the common state space modeling of battery RUL prediction, noise variances are usually assumed to be fixed. However, noise variances have great influence on state space modeling. If noise variances are too small, it takes long time for initial guess states to approach true states, and thus estimated states and measurements are biased. If noise variances are too large, state space modeling based filtering will suffer divergence. Besides, even though a same type of lithium-ion batteries are investigated, their degradation paths vary quite differently in practice due to unit-to-unit variation, ambient temperature and other working conditions. Consequently, heterogeneity of noise variances should be taken into consideration in state space modeling so as to improve RUL prediction accuracy. More importantly, noise variances should be posteriorly updated by using up-to-date battery capacity degradation measurements. In this paper, an efficient prognostic method for battery RUL prediction is proposed based on state space modeling with heterogeneity of noise variances. 26 lithium-ion batteries degradation data are used to illustrate how the proposed prognostic method works. Some comparisons with other common prognostic methods are conducted to show the superiority of the proposed prognostic method.     

Interacting multiple model particle filter for prognostics of lithium-ion batteries

We propose a new data-driven prognostic method based on the interacting multiple model particle filter (IMMPF) for determining the remaining useful life (RUL) of lithium-ion (Li-ion) batteries and the probability distribution function (PDF) of the associated uncertainty. The method applies the IMMPF to different state equations. Modeling the battery capacity degradation is very important for predicting the RUL of Li-ion batteries. In this study, improvements are made on various Li-ion battery capacity models (i.e., polynomial, exponential, and Verhulst models). Further, three different one-step state transition equations are developed, and the IMMPF method is applied to estimate the RUL of Li-ion batteries with the use of the three improved models. The PDF of the predicted RUL is obtained by combining the PDFs obtained with each individual model. We conduct four case studies to validate the proposed method. The results are as follows: (1) the three improved models require fewer parameters than the original models, (2) the proposed prognostic method shows stable and high prediction accuracy, and (3) the proposed method narrows the uncertainty PDF of the predicted RUL of Li-ion batteries.     

Robust state estimation for two-dimensional stochastic time-delay systems with missing measurements and sensor saturation

In this paper, the robust state estimation problem is investigated for a class of uncertain two-dimensional (2-D) systems with state delays and stochastic disturbances. The imperfect measurement output is subject to probabilistic data missing and sensor saturations. The missing phenomenon of the sensor measurement is governed by a stochastic variable satisfying the Bernoulli random binary distribution law, and the sensor saturation is considered to reflect the limited capacity of the communication networks. The parameter uncertainties are assumed to be norm-bounded and enter into the linear part of the system model in both directions. Through available but imperfect output measurements, a state estimator is designed to estimate the system states in the presence of data missing, sensor saturation, parameter uncertainties as well as stochastic perturbations. By defining an energy-like functional and conducting some stochastic analysis, several sufficient criteria in terms of matrix inequalities are established, which not only ensure the existence of the desired estimator gains but also guarantee the globally robustly asymptotic stability in the mean square of the estimation error dynamics. Finally, two numerical examples are exploited to show the effectiveness of the design method proposed in this paper.     

Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery

Electrical power system (EPS) is one of the most critical sub-systems of the spacecraft. Lithium-ion battery is the vital component is the EPS. Remaining useful life (RUL) prediction is an effective mean to evaluate the battery reliability. Autoregressive model (AR) and particle filter (PF) are two traditional approaches in battery prognosis. However, the parameters in a trained AR model cannot be updated which will cause the under-fitting in the long term prediction and further decrease the RUL prediction accuracy. On the other hand, the measurement function in the PF algorithm cannot be obtained in the long term prediction process. To address these two challenges, a hybrid method of IND-AR model and PF algorithm are proposed in this work. Compared with basic AR model, a nonlinear degradation factor and an iterative parameter updating method are utilized to improve the long term prediction performance. The capacity prediction results are applied as the measurement function for the PF algorithm. The nonlinear degradation factor can make the linear AR model suitable for nonlinear degradation estimation. And once the capacity is predicted, the state-space model in the PF is activated to obtain an optimized result. Optimized capacity prediction result of each cycle is utilized to re-train the regression model and update the parameters. The predictor keeps working iteratively until the capacity hit the failure threshold to calculate the RUL value. The uncertainty involved in the RUL prediction result is presented by PF algorithm as well. Experiments are conducted based on commercial lithium-ion batteries and real-applied satellite lithium-ion batteries. The results have high accuracy in capacity fade prediction and RUL prediction of the proposed method. The real applied lithium-ion battery can meet the requirement of spacecraft. All the experiments results show great potential of the proposed framework.     

Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

The prediction of Remaining useful life (RUL) and the estimation of State of health (SOH) are extremely important issues for operating performance of Lithium-ion (Li-ion) batteries in the Battery management system (BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network (WNN) with Unscented particle filter (UPF) model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform (DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries' lifespan.     

A New Dynamic-Copula Based Correlated Degradation Feature for Remaining Useful Life Prediction

Feature extraction plays an important role in Remaining useful life (RUL) prediction. Feature extraction mainly depends on the performance degradation signal in the previous study, in which the dynamic correlations among different signals are ignored, and the RUL accuracy is affected. A new dynamic feature based on the correlations of the performance degradation signal is proposed. First, dynamic correlation coefficients are calculated by copula function as the multivariate correlation performance degradation features. Second, the random effect Wiener process is used for RUL prediction based on the new features, and the maximum likelihood estimation is adopted to calculate the unknown parameters of the Wiener process. Finally, the RUL estimation for solder joints under vibration load is carried out compared with the quantile and quantile-Principal component analysis (PCA) mixed feature extraction method. The research results show that the proposed method improved the prediction accuracy of RUL.     

Accelerated degradation-tests with tightened critical values

ALT (accelerated life tests) are widely used to provide quickly the information about life distributions of products. Life data at elevated stresses are extrapolated to estimate the life distribution at design stress. The existing estimation methods are efficient and easy to implement-given sufficient life data. However, ALT frequently results in few or no failures at low-level stress, making it difficult to estimate the life distribution. For products whose failures are defined in terms of performance characteristics exceeding their critical values, reliability assessment can be based on degradation measurements by using degradation models. The estimation, however, is usually mathematically complicated and computationally intensive. This paper presents a method for the estimation of life distribution by using life data from degradation measurements. Since the time-to-failure depends on the level of a critical value, more life data can be obtained by tightening the critical value. The relationship between life and critical value and stress is modeled and used to estimate the life distribution at a usual critical value and design stress. The model parameters are estimated by using maximum likelihood. The optimum test plans, which choose the critical values, stress levels, and proportions of sample size to each stress level, are devised by minimizing the asymptotic variance of the mean (log) life at a usual critical value and design stress. The comparison between the proposed and existing 2-level test plans shows that the proposed plans have smaller asymptotic variance and are less sensitive to the uncertainty of the pre-estimates of unknown parameters.     

Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC

Lithium-ion batteries are widely used as power sources in various portable electronics, hybrid electric vehicles, aeronautic and aerospace engineering, etc. To ensure an uninterruptible power supply, the remaining useful life (RUL) prediction of lithium-ion batteries has attracted extensive attention in recent years. This paper proposed an improved unscented particle filter (IUPF) method for lithium-ion battery RUL prediction based on Markov chain Monte Carlo (MCMC). The method uses the MCMC to solve the problem of sample impoverishment in UPF algorithm. Additionally, the IUPF method is proposed on the basis of UPF, so it can also suppress the particle degradation existing in the standard PF algorithm. In this work, the IUPF method is introduced firstly. Then, the capacity data of lithium-ion batteries are collected and the empirical capacity degradation model is established. The proposed method is used to estimate the RUL of lithium-ion battery. The RUL prediction results demonstrate the effectiveness and advantage.     

Prognostics of Li(NiMnCo)O2-based lithium-ion batteries using a novel battery degradation model

Some lithium-ion battery materials show two-phase degradation behavior with evident inflection points, such as lithium nickel manganese cobalt oxide (Li(NiMnCo)O₂ or NMC) cells. A model-based Bayesian approach is proposed in this paper to predict remaining useful life (RUL) for these types of batteries. First, a two-term logarithmic model is developed to capture the degradation trends of NMC batteries. By fitting the battery degradation data, it is experimentally demonstrated that the developed model is superior to existing empirical battery degradation models. A particle filtering–based prognostic method is then incorporated into the model to estimate the batteries' possible degradation trajectories. Correspondingly, the RUL values of NMC batteries are expressed in terms of probability density function. The effectiveness of the developed method is verified with our collected experimental data. The results indicate that the proposed prognostic method can achieve higher predictive accuracy than the existing two-term exponential model.