Path classification and estimation model based prognosis of pneumatic cylinder lifetime

Prognosis is a key technology to improve reliability,safety and maintainability of products,a lot of researchers have been devoted to this technology.But to improve the predict accuracy of remaining li...     

基于复合非齐次泊松过程的不完美维修设备剩余寿命预测

针对现有不完美维修设备剩余寿命预测方法难以准确反映设备真实维修规律的问题,提出一种基于复合非齐次泊松过程的不完美维修设备剩余寿命预测方法。基于非线性Wiener过程构建设备随机退化模型;假设不完美维修次数存在上限值,并据此建立基于复合非齐次泊松过程的不完美维修模型;然后,基于设备的随机退化模型与不完美维修模型构建综合退化模型,并采用极大似然方法估计模型参数;基于首达时间的概念,推导出不完美维修设备剩余寿命的概率密度函数。实例分析表明,所提方法能够有效提升不完美维修设备剩余寿命预测的准确性,具备工程应用前景。     

A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction

Health monitoring and prognostics of equipment is a basic requirement for condition-based maintenance (CBM) in many application domains. This paper presents an age-dependent hidden semi-Markov model (HSMM) based prognosis method to predict equipment health. By using hazard function (h.f.), CBM is based on a failure rate which is a function of both the equipment age and the equipment conditions. The state values of the equipment condition considered in CBM, however, are limited to those stochastically increasing over time and those having non-decreasing effect on the hazard rate. The previous HSMM based prognosis algorithm assumed that the transition probabilities are only state-dependent, which means that the probability of making transition to a less healthy state does not increase with the age. In the proposed method, in order to characterize the deterioration of equipment, three types of aging factors that discount the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states are integrated into the HSMM. With an iteration algorithm, the original transition matrix obtained from the HSMM can be renewed with aging factors. To predict the remaining useful life (RUL) of the equipment, hazard rate is introduced to combine with the health-state transition matrix. With the classification information obtained from the HSMM, which provides the current health state of the equipment, the new RUL computation algorithm could be applied for the equipment prognostics. The performances of the HSMMs with aging factors are compared by using historical data colleted from hydraulic pumps through a case study.     

基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测

为了评估滚动轴承的可靠性和预测剩余使用寿命,选取能够反映性能退化过程的特征参数作为寿命预测模型的输入参数,提出一种基于核主元分析(kernel principal component analysis,简称KPCA)和威布尔比例故障率模型(Weibull proportional hazards model,简称WPHM)的方法。首先,提取滚动轴承全寿命周期的时域、频域及时频域等多特征参数,从中筛选出有效的特征参数,构建高维相对特征集;其次,进行核主元分析,选取能够反映轴承全寿命周期性能退化过程的核主元,进而作为WPHM的协变量来进行可靠性评估和剩余寿命预测。通过滚动轴承全寿命试验,验证了该方法能够对轴承进行准确的可靠性评估和剩余寿命预测,以提供及时的维修决策。同时,由于提取的是相对特征,降低了同种轴承间在制造、安装及工况的差异,增强了该方法的适用性和稳定性。     

数字孪生驱动的航空发动机涡轮盘剩余寿命预测

为解决航空发动机涡轮盘剩余寿命在线预测难题,提出一种数字孪生驱动的涡轮盘剩余寿命预测方法。在建立数字孪生模型的过程中,首先,分析涡轮盘疲劳裂纹损伤机理,构建性能退化指标,建立涡轮盘性能退化过程的共性表征模型;其次,分析多种不确定性因素,采用状态空间模型建立涡轮盘性能退化过程的个性表征模型;然后,通过动态贝叶斯网络描述状态空间模型随时间的演化规律,建立涡轮盘性能退化过程的动态演化模型;最后,采用粒子滤波算法实现涡轮盘退化状态追踪和剩余寿命预测,从而完成涡轮盘性能退化数字孪生模型的建立。融合涡轮盘实时传感数据,通过贝叶斯推理实现对该数字孪生模型的动态更新。通过某型涡轮盘试验数据对该方法进行验证,结果表明该数字孪生模型能够较好地解决涡轮盘剩余寿命在线预测问题。     

基于性能退化模型的万能式断路器操作附件实时剩余寿命预测

针对万能式断路器操作附件的个体差异性以及在实际使用过程中动作不频繁的特性,提出一种基于性能退化模型的万能式断路器操作附件实时机械剩余寿命(RUL)预测方法。不同于传统的RUL预测方法,该方法融合了操作附件的历史退化数据与实时更新的状态监测(CM)数据。首先,考虑到操作附件性能退化过程具有线性非单调的特点,建立基于Wiener过程的操作附件性能退化模型;其次,对操作附件的历史退化数据采用极大似然估计法和一维搜索法确定模型参数的先验分布;然后,运用贝叶斯方法并结合操作附件实时更新的CM信息对模型参数进行迭代更新;基于首达时间的概念建立了RUL预测模型,以实现对断路器操作附件实时RUL的预测。最后,通过操作附件的寿命数据对本文所提方法进行验证,结果表明本文方法不仅可实现操作附件的实时剩余机械寿命预测,同时相较于其他文献方法具有更高的预测精度。     

Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings

The remaining useful life(RUL) estimation of bearings is critical for ensuring the reliability of mechanical systems. Owing to the rapid development of deep learning methods, a multitude of data-driven RUL estimation approaches have been proposed recently. However, the following problems remain in existing methods: 1) Most network models use raw data or statistical features as input, which renders it di cult to extract complex fault-related information hidden in signals; 2) for current observations, the dependence between current states is emphasized, but their complex dependence on previous states is often disregarded; 3) the output of neural networks is directly used as the estimated RUL in most studies, resulting in extremely volatile prediction results that lack robustness. Hence, a novel prognostics approach is proposed based on a time–frequency representation(TFR) subsequence, three-dimensional convolutional neural network(3 DCNN), and Gaussian process regression(GPR). The approach primarily comprises two aspects: construction of a health indicator(HI) using the TFR-subsequence–3 DCNN model, and RUL estimation based on the GPR model. The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy. Subsequently, the 3 DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs. Finally, the RUL of the bearings is estimated using the GPR model, which can also define the probability distribution of the potential function and prediction confidence. Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence–3 DCNN–GPR approach. The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.     

锂离子电池循环寿命的融合预测方法

针对传统基于粒子滤波的锂离子电池剩余使用寿命预测方法的不足:过度依赖电池经验退化模型和模型输入变量单一的问题,提出了一种相关向量机、粒子滤波和自回归模型融合的锂离子电池剩余寿命预测的方法。通过相关向量机提取电池历史数据的退化趋势,构建趋势方程替换以往的电池经验退化模型,作为粒子滤波算法的状态转换方程。引入自回归模型的长期趋势预测值,替换观测值构建粒子滤波算法的观测方程。将3种方法相融合估计电池剩余寿命。实验结果表明:融合方法不仅预测精度高而且采用数据驱动的方法避免了构建复杂的电池机理退化模型,通用性强。     

基于变分模态分解与集成深度模型的锂电池剩余寿命预测方法

锂电池剩余寿命(RUL)预测对于锂电池安全使用至关重要.由于锂电池使用过程中存在容量再生现象和随机干扰等因素,导致单一尺度信号下单一模型的预测精度及泛化性能较差.针对上述问题,提出一种新的基于变分模态分解(VMD)与集成深度模型的锂电池剩余寿命预测方法.首先,采用变分模态分解将锂电池容量数据进行多尺度分解,得到信号的全...     

Application of grey model for machine degradation prognostics

Predicting machine degradation before final failure occurs is very important. This paper presents a method to predict the future state of machine degradation based on grey model and one-step-ahead forecasting technique. Specifically, the feasibility of grey model as a predictor for machine degradation prognostics system has been investigated. Grey model GM(1,1) has employed to forecast the future state of machine degradation, but the result is not satisfactory. Finally, a modification of GM(1,1) has made to improve the accuracy of prediction. However the model was built by using only four input data, it is able to track closely the sudden change of machine degradation condition. Real trending data of low methane compressor acquired from condition monitoring routine are employed for evaluating the proposed method.     

基于状态空间模型的飞机APU在翼RUL预测方法

为解决利用飞机辅助动力装置(APU)在翼监测数据难以表征其性能状态而造成的性能评估以及剩余使用寿命预测(RUL)难的问题,本文提出一种基于状态空间模型(SSM)与卡尔曼滤波融合的APU在翼RUL预测方法.首先,通过在翼监测数据构造含噪声的性能指标(PI)来表征APU的性能状态,借助维纳过程与建立的含噪声的PI构建状态方...     

一种融合多传感器数据的数模联动机械剩余寿命预测方法

随着传感和信息技术的发展,各式各样的传感器获取了机械装备海量的监测数据,让剩余寿命预测有"据"可依,推动机械剩余寿命预测进入了大数据时代。但由于数据类型多样、量大面广,如何利用丰富的多传感器数据,从中快速挖掘健康状态退化信息,指导寿命预测,成为大数据时代下机械寿命预测的全新挑战。基于模型的寿命预测方法大多仅针对单一监测数据进行建模分析,无法有效利用丰富的大数据资源。数据驱动的方法则过分依赖训练数据,缺乏必要的经验指引,方法的可解释性差。为了有效利用多传感器数据指导寿命预测,从数模联动的思路出发,建立了一种融合多传感器数据的数模联动寿命预测方法。采用一种通用的Wiener过程模型对健康状态退化过程进行描述,分别建立多源观测函数和多源映射函数对状态与数据之间的因果关系和关联关系进行描述,采用粒子滤波算法将多传感器数据与模型进行动态匹配,预测剩余寿命。在提出方法的统一框架指导下,选取三种特定模型对铣刀剩余寿命进行预测,验证了提出方法的有效性。     

基于Wiener过程的万能式断路器附件剩余寿命预测*

万能式断路器作为一个复杂的机械系统,其操作附件的剩余寿命预测对于维护断路器的可靠性至关重要。为准确掌握操作附件剩余寿命情况,提出了一种基于Wiener过程的万能式断路器操作附件剩余机械寿命预测方法。首先,通过对操作附件动作过程中线圈电流波形的分析选取了动作时间作为性能退化特征量;其次,考虑到断路器操作附件性能退化过程具有线性非单调的特点,采用Wiener过程建立了操作附件的性能退化模型,并利用极大似然估计法对退化模型参数进行估计;然后,基于首达时间的概念建立了剩余寿命预测模型,推导出剩余寿命概率密度函数解析式。最后对安装于万能式断路器上的分励脱扣器和释能电磁铁两种操作附件进行全寿命试验及其剩余寿命预测,预测结果验证了所提方法的有效性。     

A degradation-based model for joint optimization of burn-in, quality inspection, and maintenance: a light display device application

This paper presents a degradation-based model to jointly determine the optimal burn-in, inspection, and maintenance decisions, based on degradation analysis and an integrated quality and reliability cost model. Degradation modeling plays an important role in reliability prediction and analysis for many highly reliable components and equipment, when the failures can rarely be observed. Unlike traditional applications, quality and reliability must be considered simultaneously for devices subject to degradation, because quality inspection decisions often impact anticipated reliability and failure-time distributions. This paper presents an integrated model to jointly optimize quality and reliability for devices subject to degradation, with a focus on burn-in, quality inspection, and maintenance policies. Based on the degradation modeling and analysis, the reliability function and the time-to-failure distribution are derived under the condition that the quality inspection is applied following the burn-in period. The optimal burn-in, quality inspection, and preventive maintenance policies are determined by minimizing the expected total cost per usage lifetime. The proposed model is illustrated using the application of light display devices, in which the degradation path follows a negative shifted lognormal distribution with a random failure threshold. A numerical example is provided to illustrate the application of our model to the light display devices.     

退化数据驱动的气缸剩余寿命在线预测

针对气缸可靠性研究中剩余寿命预测方面的问题,提出了一种基于退化路径的气缸剩余寿命在线预测方法。在建立了基于维纳过程的气缸退化模型基础上,推导了退化路径决定下的气缸剩余寿命的概率密度函数解析表达式,提出了一种融合Bayes估计和期望最大化算法的参数在线估计方法,实现了气缸剩余寿命在线预测,并通过气缸性能退化实验数据验证了方法的有效性。通过与同类方法对比结果表明,在小样本情况下,所提方法能更准确地预测气缸剩余寿命且预测的不确定性更低。     

Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method

The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). These studies incorporated many di erent models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and di culty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model(SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation(sequential Monte Carlo). Being e ective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM(condition based maintenance), PHM(prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.     

动态贝叶斯网络在设备剩余寿命预测中的应用研究

采用动态贝叶斯网络对设备剩余寿命进行预测,建立了基于动态贝叶斯网络模型的设备剩余寿命预测框架模型,运用动态贝叶斯网络的粒子滤波近似推理算法对加工过程中钻头寿命预测进行实例研究,结果表明了该方法的有效性.     

机载液压系统寿命预测与估计

寿命预测和估计是机载液压系统健康管理的核心难点。综述了机载系统寿命预测与估计常用方法,针对长寿命机载系统不可能在出厂前给出准确寿命,提出动态数据更新的粒子滤波寿命估计方法。建立机载液压系统多场耦合作用下性能退化规律,将研制寿命试验的累积损伤表征在退化状态内部,实际飞行数据通过贝叶斯滤波动态更新到寿命估计模型中,考虑余度液压系统多退化状态增广,给出动态机载液压系统寿命预测与估计方法,实现机载液压系统高精度寿命估计。     

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.     

A dynamic multi-scale Markov model based methodology for remaining life prediction

The ability to accurately predict the remaining life of partially degraded components is crucial in prognostics. In this paper, a performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, an improved Markov model is proposed for remaining life prediction. Fuzzy C-Means (FCM) algorithm is employed to perform state division for Markov model in order to avoid the uncertainty of state division caused by the hard division approach. Considering the influence of both historical and real time data, a dynamic prediction method is introduced into Markov model by a weighted coefficient. Multi-scale theory is employed to solve the state division problem of multi-sample prediction. Consequently, a dynamic multi-scale Markov model is constructed. An experiment is designed based on a Bently-RK4 rotor testbed to validate the dynamic multi-scale Markov model, experimental results illustrate the effectiveness of the methodology.