线性FBM过程随机退化设备剩余寿命自适应预测

现有文献主要通过马尔科夫过程来描述设备的退化轨迹,鲜有考虑历史状态对未来退化轨迹的影响.鉴于此,为了反映退化轨迹的记忆效应对剩余寿命预测的影响,首先基于分数布朗运动建立一种线性随机退化模型,并通过引入随机效应来反映不同样本间的退化差异性;其次,基于弱收敛性理论与分数布朗运动的特性推导剩余寿命分布的近似解析解;再次,基于...     

融合多源数据的非线性退化建模与剩余寿命预测

针对目前基于单个传感器剩余寿命预测方法存在预测精度不高的问题,该文提出一种融合多源传感器数据的非线性退化建模与剩余寿命预测方法。该方法包括复合健康指标的构建、模型参数的估计和传感器融合系数的确定,在确定融合系数后,结合设备历史寿命数据与实时监测数据,利用Bayesian参数更新公式推导出设备的剩余寿命概率分布,实现设备的剩余寿命在线预测。最后通过由商用模块化航空推进系统仿真生成的发动机退化数据集进行仿真实验,结果表明该文所提方法能够有效提高设备剩余寿命预测的准确性。     

基于Gamma退化过程的直升机主减速器行星架剩余寿命预测模型

鉴于Gamma过程具有平稳、独立增量等退化建模所需的属性,将其用于描述设备退化过程,并针对缺乏故障数据时难以进行剩余寿命预测的问题,利用设备运行中采集的表征其退化状态的大量间接状态参数和少量直接状态参数,建立了基于Gamma退化过程的剩余寿命预测模型;针对经验最大化算法中似然函数难以解析求解的问题,引入粒子滤波算法实现了模型参数估计;最后将模型应用于直升机主减速器行星架的剩余寿命预测,得到了不同时刻的预测结果及95%置信区间,验证了预测模型的有效性和准确性。     

基于二元混合随机过程的轴承剩余寿命预测

针对轴承退化问题,基于两个不同变化特点的性能指标,提出了基于二元混合随机过程的轴承剩余寿命(RUL)预测方法。首先,分析两个特点不同的性能指标,选择合适的随机过程(伽马过程或维纳过程)分别构建基于不同性能指标的退化模型;其次,利用Copula函数分析两个性能指标间的相关特性并构建剩余寿命的联合概率密度函数;然后采用分步极大似然估计法在线更新模型参数,预测未来时刻的剩余寿命;最后,通过仿真和轴承实验数据对所提方法进行验证分析。结果显示所提方法能有效地预测轴承的剩余寿命,通过与基于一元随机过程的剩余寿命预测方法的对比分析,发现所提方法具有更好的预测精度。     

现有混凝土桥梁结构剩余寿命预测方法研究

在回顾桥梁剩余寿命预测方法的基础上,探讨了利用人工神经网络对现有混凝土桥梁结构剩余寿命进行全桥模糊预测的方法。该方法从全桥的角度综合考虑各种因素影响,利用因果分析图及数学模糊规则,事先归纳出影响混凝土桥梁整体寿命的几种主要因素,并利用自编程序对几种主要因素的相关数据进行神经网络训练。训练结果表明,只要网络结构选择合理,配合正常的检测制度与专门的数据采集系统,其对全桥剩余寿命进行模糊预测的结果是具有实用价值的。     

考虑棘轮效应的疲劳寿命预测方法

多轴棘轮加载时轴向加载的恒定应力、剪切应变幅对轴向棘轮应变和疲劳寿命有很大的影响.考虑棘轮效应影响的Coffin模型将棘轮效应与循环部分相结合来计算疲劳寿命,预测结果较好,绝大部分预测结果分布在2倍分散带内.     

齿轮箱状态识别与剩余寿命预测的MoG-BBN法

基于混合高斯输出贝叶斯信念网络模型的齿轮箱退化状态识别与剩余寿命预测新方法,应用聚类评价指标对全寿命过程退化状态数进行优化,通过计算待识别故障特征向量的概率值来确定齿轮箱退化状态,在退化状态识别的基础上,提出了剩余寿命计算方法。再利用齿轮箱全寿命实验数据对此进行验证。结果表明,该方法可以有效地识别齿轮箱故障状态并实现剩余寿命预测,平均预测正确率为96.47 %,为齿轮箱的健康管理提供参考。     

浅析锂电池剩余寿命的ELM间接预测方法

锂离子电池在实际的使用过程中经常会出现性能退化现象,电池性能退化必然会影响到仪器设备的正常使用,严重时可能会引起设备故障,因此电池使用过程中通常会使用一定的方法对锂离子电池的剩余使用寿命进行预测,现阶段常用的锂离子电池剩余寿命预测主要有两类方法,但这两种方法都存在着一些问题,因此本文构建一种ELM间接预测的方法,本文将对这种方法进行详细地介绍。     

双指数粒子滤波模型的硅泡沫材料剩余寿命预测

针对传统基于模型的硅泡沫材料长时使用寿命评估方法存在的物理模型解释性差、预测精度不高等问题,本文提出了一种双指数粒子滤波模型的剩余寿命预测方法。选取硅泡沫结构的载荷保持率作为特征量,基于硅泡沫材料的应力松弛失效机制,建立了更具解释性的双指数应力退化模型。首先利用最小二乘法对观测数据进行拟合,初始化模型参数和健康状态,然后通过贝叶斯理论对历史样本进行状态跟踪建模,更新状态传递函数,实现载荷保持率退化趋势预测和剩余寿命评估。通过仿真和实验验证了双指数粒子滤波模型预测硅泡沫材料剩余寿命的泛化适用性和准确性,同时与传统指数模型预测结果进行了对比,结果表明本文所提方法预测精度和稳定性更优。     

在役金属材料剩余寿命预测技术

本文论述了预测在役金属材料剩余寿命的重要性 ,并介绍了利用蠕变空洞对在役材料剩余寿命进行定性和定量的预测技术     

Remaining useful life prediction for multivariable stochastic degradation systems with non-Markovian diffusion processes

Multivariable stochastic degradation system (MSDS) is quite common in industries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large-scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non-Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi-sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed-form distribution of system-level remaining useful life (RUL) is obtained with a mild two-layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method.     

Remaining useful life prediction for hard failures using joint model with extended hazard

In this paper, we investigate a joint modeling method for hard failures where both degradation signals and time‐to‐event data are available. The mixed‐effects model is used to model degradation signals, and extended hazard model is used for the time‐to‐event data. The extended hazard is a general model which includes two well‐known hazard rate models, the Cox proportional hazards model and accelerated failure time model, as special cases. A two‐stage estimation approach is used to obtain model parameters, based on which remaining useful life for the in‐service unit can be predicted. The performance of the method is demonstrated through both simulation studies and a real case study.     

周期应力下非线性退化建模与剩余寿命估计

针对设备在贮存和工作过程中可能存在受到周期性特征应力作用的问题,基于扩散过程,提出一种周期应力作用下的非线性退化模型,并在首达时间意义下,推导得到解析形式的剩余寿命分布函数,给出参数的极大似然估计方法。仿真实验的统计分析验证所提模型的正确性,并说明所提剩余寿命估计方法优于线性建模方法,具有潜在工程应用价值。     

Remaining fatigue life estimation of corroded bridge members

This paper presents a method to estimate remaining fatigue lives of railway bridge members subjected to time‐dependent corrosion. The method addresses effects of material loss due to general (uniform) corrosion and fatigue strength degradation of material due to corrosive environment. The method mainly consists of stress history, which is obtained by considering the effect of time‐dependent loss of material, full‐range S–N curve, which represents the corrosive environment, and sequential law, which takes the loading sequence effect more precisely in to account than Miner's rule. Initially, nonlinear behaviour of material loss over time (i.e. time‐dependent growth of corrosion wastage) is discussed, and hence, necessary formulae to calculate time‐dependent cross‐sectional properties are comprehensively presented. Then finite element analysis‐based procedure is clearly proposed to predict stress histories of corroded members. A technique is introduced to obtain the full‐range S–N curve for the corroded structural detail. The concept of sequential law is summarized with the algorithm, and then the proposed method is applied to predict the remaining fatigue lives of the corroded members of a railway bridge. The predicted remaining lives were compared with the previous method‐based estimations, and comparisons reveal the range of 16–47% reduction of fatigue lives of critical members when time‐dependent corrosion is taken into account. Also, the results reveal that the corroded members of smaller cross‐sectional area are most vulnerable for fatigue damage. Finally, significance of the proposed method is confirmed.     

A Wiener‐based degradation model with logistic distributed measurement errors and remaining useful life estimation

Degradation data analysis, which investigates degradation processes of products to extrapolate the lifetime properties, is an effective method for reliability analysis. But degradation data that reflect a product's inherent randomness of degradation are often contaminated by measurement errors. To deal with the problem, this paper proposes a Wiener‐based model with an assumption of logistic distributed measurement errors and adopts the Monte Carlo expectation‐maximization method together with the Gibbs sampling for parameter estimation. Based on the model and parameter estimates, an efficient algorithm is proposed for a quick calculation of maximum likelihood value. Also, the estimation of remaining useful lifetime is discussed. Simulation results show that the proposed model is relatively better and more robust in comparison with the Wiener process with Gaussian noises. Finally, the application of the proposed model is illustrated by an example.     

粒子滤波参数估计方法在齿轮箱剩余寿命预测中的应用研究

针对非线性非高斯系统的剩余寿命（RUL）预测问题,本文提出了一种基于粒子滤波（PF）理论的设备剩余寿命预测方法。首先建立设备的非线性状态空间模型（含有未知的时变参数）,然后通过粒子滤波算法估计出设备状态的概率密度函数（PDF）,从而根据该PDF计算出设备的RUL。此外,计算设备RUL的期望值和95%置信区间,并对模型的预测效果进行评估,验证预测的有效性和准确性。最后通过齿轮箱的全寿命实验,对本文所提方法的有效性进行实例验证,将实验结果和传统的比例风险模型（PHM）预测结果对比分析,结果表明本文提出的剩余寿命预测方法要优于传统的PHM预测方法。     

Remaining useful life prediction of lithium-ion battery using a novel health indicator

Remaining useful life (RUL) prediction plays a significant role in the health prognostic of lithium-ion batteries (LIBs). The capacity or internal resistance is commonly used to quantify degradation process and predict RUL of LIB, but those two indicators are difficult to be obtained due to complex operational conditions and high costs, respectively. To address this issue, we extract a novel health indicator (HI) from the battery current profiles that can be directly measured online. Furthermore, the indicator is optimized by Box-Cox transformation and evaluated by correlation analysis for degradation modeling accurately. Finally, relevance vector machine (RVM) algorithm is utilized to make a probabilistic prediction for battery RUL based on the extracted HI. The correlation analysis verifies the effectiveness of the novel HI, and comparative experiments demonstrate the proposed method can predict RUL of LIB more accurately.     

Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter

ABSTRACT

A novel RUL prediction approach for lithium-ion batteries using quantum particle swarm optimization (QPSO)-based particle filter (PF) is proposed. Compared to particle swarm optimization (PSO)-based PF, QPSO-based PF is proved to have a better performance in global searching and has fewer parameters to control, which makes QPSO-PF easier for applications. Moreover, fewer particles are required by QPSO-PF to accurately track the battery's health status, leading to a reduction of computation complexity. RUL prediction results using real data provided by NASA and compared with benchmark approaches demonstrates the superiority of the proposed approach.     

A new frequency domain method for random fatigue life estimation in a wide‐band stationary Gaussian random process

A new frequency domain method for random fatigue life estimation in a wide‐band stationary Gaussian random process was proposed for application in fatigue analysis. Simulations of the power spectral densities of different types were firstly performed; the simulated results showed that the accuracy and applicability for the current frequency domain methods are not only related to the spectral type but also associated with the types of the analysed materials. Compared with the current methods, the proposed method, in which the rain‐flow amplitude obeys Nakagami distribution, has better universality and could significantly reduce the error for the random fatigue life estimation with simulated and actual spectra. Verified application in cast‐steel fatigue life analysis were performed between random fatigue life and constant amplitude fatigue life. It is shown that the fatigue life analysis under random load cannot be ignored and the proposed new method can serve as a recommended method.