Remaining useful life prediction based on variation coefficient consistency test of a Wiener process

High-cost equipment is often reused after maintenance, and whether the information before the maintenance can be used for the Remaining Useful Life (RUL) prediction after the maintenance is directly determined by the consistency of the degradation pattern before and after the maintenance. Aiming at this problem, an RUL prediction method based on the consistency test of a Wiener process is proposed. Firstly, the parameters of the Wiener process estimated by Maximum Likelihood Estimation (MLE) are proved to be biased, and a modified unbiased estimation method is proposed and verified by derivation and simulations. Then, the h statistic is constructed according to the reciprocal of the variation coefficient of the Wiener process, and the sampling distribution is derived. Meanwhile, a universal method for the consistency test is proposed based on the sampling distribution theorem, which is verified by simulation data and classical crack degradation data. Finally, based on the consistency test of the degradation model, a weighted fusion RUL prediction method is presented for the fuel pump of an airplane, and the validity of the presented method is verified by accurate computation results of real data, which provides a theoretical and practical guidance for engineers to predict the RUL of equipment after maintenance.     

基于非线性Wiener过程的航空发动机性能衰减建模与剩余寿命预测

针对航空发动机在性能衰减过程中普遍存在的非线性和三源不确定性问题,提出了一种基于非线性Wiener过程的航空发动机性能衰减建模与剩余寿命（RUL）预测方法。首先,为解决目前大多数剩余寿命预测方法中潜在假设的局限性,即当前时刻估计的漂移系数与上一时刻漂移系数的后验估计完全相等,在状态空间模型的框架下建立了一类新的同时考虑非线性和三源不确定性的性能衰减模型,并在首达时间下推导出剩余寿命的分布。然后,针对新研发航空发动机缺乏历史数据和先验信息的问题,提出了一种基于Kalman滤波和条件期望最大化（ECM）算法的参数估计方法,使得估计的模型参数不依赖于历史数据量。同时能够在获得一个新的性能衰减数据后,实现对模型参数的自适应估计和在线更新,进而实时地更新航空发动机的剩余寿命分布。实验结果表明,本文方法可以有效地提高剩余寿命预测的准确性,能为航空发动机的维修决策提供可靠的依据。     

基于RF-SVR的燃油计量装置性能衰退检测和剩余寿命估计方法

为了实现航空发动机燃油系统的安全状态监测和健康管理,开展了燃油系统性能衰退检测和剩余使用寿命估计方面的研究。以燃油系统燃油计量装置为例,分析了其主要的性能衰退模式,设计了基于电流-速度数据的健康指标(HIs)选取方案,并考虑环境及模型参数不确定性,进行模型不确定性仿真,基于健康数据与性能衰退数据间的马氏距离对部件性能衰退进行检测。提出了基于随机森林-支持向量回归(RF-SVR)的剩余使用寿命(RUL)估计方法,利用通过RF特征选择优化的SVR模型实现部件RUL估计。最后基于某型民用涡扇发动机机械液压模型仿真数据对该方法进行了验证,结果表明:该方法的性能衰退检测虚警率及漏报率低于2%,RUL估计误差低于3%,可为航空发动机燃油系统的预测性维护提供参考。      

Remaining useful life estimation for deteriorating systems with time-varying operational conditions and condition-specific failure zones

Dynamic time-varying operational conditions pose great challenge to the estimation of system remaining useful life (RUL) for the deteriorating systems. This paper presents a method based on probabilistic and stochastic approaches to estimate system RUL for periodically moni-tored degradation processes with dynamic time-varying operational conditions and condition-specific failure zones. The method assumes that the degradation rate is influenced by specific oper-ational condition and moreover, the transition between different operational conditions plays the most important role in affecting the degradation process. These operational conditions are assumed to evolve as a discrete-time Markov chain (DTMC). The failure thresholds are also determined by specific operational conditions and described as different failure zones. The 2008 PHM Conference Challenge Data is utilized to illustrate our method, which contains mass sensory signals related to the degradation process of a commercial turbofan engine. The RUL estimation method using the sensor measurements of a single sensor was first developed, and then multiple vital sensors were selected through a particular optimization procedure in order to increase the prediction accuracy. The effectiveness and advantages of the proposed method are presented in a comparison with exist-ing methods for the same dataset.     

Remaining useful life prediction based on the Wiener process for an aviation axial piston pump

An aviation hydraulic axial piston pump’s degradation from comprehensive wear is a typical gradual failure model. Accurate wear prediction is difficult as random and uncertain char-acteristics must be factored into the estimation. The internal wear status of the axial piston pump is characterized by the return oil flow based on fault mechanism analysis of the main frictional pairs in the pump. The performance degradation model is described by the Wiener process to predict the remaining useful life (RUL) of the pump. Maximum likelihood estimation (MLE) is performed by utilizing the expectation maximization (EM) algorithm to estimate the initial parameters of the Wiener process while recursive estimation is conducted utilizing the Kalman filter method to estimate the drift coefficient of the Wiener process. The RUL of the pump is then calculated accord-ing to the performance degradation model based on the Wiener process. Experimental results indi-cate that the return oil flow is a suitable characteristic for reflecting the internal wear status of the axial piston pump, and thus the Wiener process-based method may effectively predicate the RUL of the pump.     

多传感器监测飞机部件非线性退化评估

飞机部件一般采用多传感器进行状态监控,针对退化过程具有非线性特征的民机典型部件剩余寿命（RUL）预测及评估问题,首先建立了部件性能参数的一般非线性Wiener退化过程,推导出基于多传感器监测数据的剩余寿命预测框架和概率密度函数,随后利用状态空间模型进行隐退化状态估计并同时利用最大期望算法（EM）实现参数递推估计,最后形成了飞机部件多传感器监测下的剩余寿命非线性退化评估方法。通过数值仿真案例和民航发动机剩余寿命预测案例,对比线性退化模型和基于单一传感器监测数据的非线性退化模型,验证了所提方法在提高剩余寿命预测精度的有效性,可为飞机及其部件的剩余使用寿命预测和视情维护决策提供技术支撑。     

Remaining useful life prognostics for aeroengine based on superstatistics and information fusion

Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.     

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.     

An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps

An accurate estimation of the remaining useful life (RUL) not only contributes to an effective application of an aviation piston pump, but also meets the necessity of condition based maintenance (CBM). For the current RUL evaluation methods, a model-based method is inappropriate for the degradation process of an aviation piston pump due to difficulties of modeling, while a data-based method rarely presents high-accuracy prediction in a long period of time. In this work, an adaptive-order particle filter (AOPF) prognostic process is proposed aiming at improving long-term prediction accuracy of RUL by combining both kinds of methods. A dynamic model is initialized by a data-driven or empirical method. When a new observation comes, the prior state distribution is approximated by a current model. The order of the current model is updated adaptively by fusing the information of the observation. Monte Carlo simulation is employed for estimating the posterior probability density function of future states of the pump’s degradation. With updating the order number adaptively, the method presents a higher precision in contrast with those of traditional methods. In a case study, the proposed AOPF method is adopted to forecast the degradation status of an aviation piston pump with experimental return oil flow data, and the analytical results show the effectiveness of the proposed AOPF method.     

多退化变量下基于Copula函数的陀螺仪剩余寿命预测方法

针对惯性导航系统中陀螺仪多退化变量条件下的剩余寿命（RUL）预测问题,提出了一种基于Copula函数的多退化变量剩余寿命预测方法。首先,针对退化变量间不同的退化轨迹,采用不同的方法进行退化建模,并对于陀螺漂移系数样本标准差数据波动性随时间递增的特性,提出了一种方差时变的正态随机过程退化建模方法,得到了陀螺仪剩余寿命的边缘分布函数。然后,通过Copula函数来描述退化变量之间的相关性,将得到的剩余寿命的边缘分布进行融合,得到了陀螺仪剩余寿命的联合分布函数。最后,通过陀螺仪实例分析验证了方法的适用性和可行性。     

Remaining useful life prediction of aircraft lithium-ion batteries based on F-distribution particle filter and kernel smoothing algorithm

As an emergency and auxiliary power source for aircraft, lithium (Li)-ion batteries are important components of aerospace power systems. The Remaining Useful Life (RUL) prediction of Li-ion batteries is a key technology to ensure the reliable operation of aviation power systems. Particle Filter (PF) is an effective method to predict the RUL of Li-ion batteries because of its uncertainty representation and management ability. However, there are problems that particle weights cannot be updated in the prediction stage and particles degradation. To settle these issues, an innovative technique of F-distribution PF and Kernel Smoothing (FPFKS) algorithm is proposed. In the prediction stage, the weights of the particles are dynamically updated by the F kernel instead of being fixed all the time. Meanwhile, a first-order independent Markov capacity degradation model is established. Moreover, the kernel smoothing algorithm is integrated into PF, so that the variance of the parameters of capacity degradation model keeps invariant. Experiments based on NASA battery data sets show that FPFKS can be excellently applied to RUL prediction of Li-ion batteries.     

基于随机Wiener过程的航空发动机剩余寿命预测

针对目前剩余寿命(RL)预测方法没有综合考虑发动机个体性能退化的差异性和多阶段性的问题,提出了基于多阶段性能退化模型预测航空发动机剩余寿命的方法。首先,该方法采用多阶段Wiener过程对航空发动机进行退化建模,并假设退化模型参数服从随机分布来描述发动机个体的差异性。然后,根据历史性能退化数据与历史失效时间数据,利用期望最大化算法对模型参数的先验分布进行估计。当获得单台发动机的实时退化数据后,使用Bayesian方法对模型参数进行更新,从而实时更新航空发动机的RL分布,最终实现对单台航空发动机的RL预测。实验结果表明,该方法预测精度较高,能为航空发动机维修计划的制定提供依据。     

A Novel PF-LSSVR-based Framework for Failure Prognosis of Nonlinear Systems with Time-varying Parameters

Particle filtering (PF) is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the traditional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR framework based on PF and least squares support vector regression (LSSVR) for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction uncertainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life (RUL) is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.     

潜在故障期内航空发动机的剩余寿命预测

在航空发动机的潜在故障期内,状态参数的变化规律可以间接地预测发动机的剩余寿命。针对潜在故障期内的特点,利用随机滤波理论建立了航空发动机的剩余寿命预测模型,并采用K-M法检验发动机性能参数的退化数据服从威布尔分布,按照最优线性无偏估计的点估计方法和极大似然函数法对相关参数进行点估计,举例对某型航空发动机在潜在故障期内的剩余寿命进行了预测。模型有效地预测了航空发动机的剩余寿命,为航空发动机的视情维修提供了决策依据。     

部分可观测信息条件下装备剩余寿命预测

 针对部分可观测信息条件下退化系统的剩余寿命(RUL)预测问题,综合利用装备的历史寿命信息和性能退化信息,采用隐马尔可夫模型(HMM)对系统进行状态评估,得到系统的转移概率矩阵和观测概率矩阵;采用Bayes方法不断更新系统状态空间的条件概率分布;利用比例故障率模型(PHM)对系统进行可靠性分析,得到系统的故障率和可靠度函数,进而得到装备的剩余寿命分布。研究表明,该方法可较准确地预测装备的剩余寿命,为保障人员提供科学的维修决策依据。     

基于信息融合的航空发动机剩余寿命预测

利用航空发动机状态监测信息,考虑到信息本身具有的误差性和随机性等特点,采用贝叶斯线性模型融合了监测信息,实现了综合利用多源信息的进行航空发动机性能衰退评估;以性能衰退评估结果为输入变量,建立基于Gamma随机过程的可靠性评估模型,预测在指定性能可靠性水平下的剩余寿命.通过算例,分析了不同监测参数对剩余寿命预测的影响.该方法能将性能监测与可靠性分析集成到一个框架中,充分利用了多种状态监测信息,结果更加准确,更符合控制航空发动机维修决策风险的实际.      

基于EM-KF算法的直升机主减速器剩余寿命预测方法

为解决间接状态监测数据下直升机主减速器剩余寿命预测难以估算的难题,提出了一种卡尔曼滤波和期望最大化算法相结合的剩余寿命预测方法.该方法可以根据不断更新振动信号特征值迅速且有效地估计出模型参数,进而预测不同运行时间主减速器的剩余寿命分布,最后对主减速器试验数据进行了案例分析.结果表明:该方法能够有效估计主减速器的剩余寿命分布,通过与主减速器剩余寿命准确值对比发现,剩余寿命准确值绝大多数落于剩余寿命预测值的95%置信区间内,表明该方法具有好的准确性,进而避免故障的发生.      

基于半随机滤波-期望最大化算法的剩余寿命在线预测

剩余寿命(RL)预测是设备预测维护的关键环节。准确在线预测能够为维护策略的实时安排提供更加精确的技术支持,有效避免失效的发生。工程实际中,反映设备退化过程的性能指标往往不能直接监测,为解决隐含退化过程的剩余寿命在线预测问题,提出一种基于半随机滤波-期望最大化(EM)算法的预测方法。首先以剩余寿命为隐含状态,构建状态空间模型描述直接监测数据与设备剩余寿命间的随机关系。为实现单个设备剩余寿命的在线预测,依据到当前时刻为止的监测数据,采用扩展卡尔曼滤波(EKF)与期望最大化算法相互协作的方法实时估计与更新模型未知参数和剩余寿命分布。最后,将该方法用于惯性测量组合(IMU)剩余寿命在线预测问题,实验结果表明该方法能够提高预测的准确性并减少预测的不确定性。     

基于贮存退化机理的长贮产品序贯检测方法

为了确定发动机等长贮产品满足一定贮存可靠度要求的最优检测方式,提出了基于贮存退化机理的序贯检测方法.分析了长贮产品贮存寿命的退化机理,即不可修部件的自然老化和可修部件的检测均可能造成贮存寿命的退化;然后基于贮存退化机理给出了Weibull分布产品贮存可靠度模型及模型参数估计的最小二乘方法;进一步,根据贮存可靠度要求,给...     

Remaining useful life prediction for engineering systems under dynamic operational conditions: A semi-Markov decision process-based approach

For critical engineering systems such as aircraft and aerospace vehicles, accurate Remaining Useful Life(RUL) prediction not only means cost saving, but more importantly, is of great significance in ensuring system reliability and preventing disaster. RUL is affected not only by a system's intrinsic deterioration, but also by the operational conditions under which the system is operating. This paper proposes an RUL prediction approach to estimate the mean RUL of a continuously degrading system under dynamic operational conditions and subjected to condition monitoring at short equi-distant intervals. The dynamic nature of the operational conditions is described by a discrete-time Markov chain, and their influences on the degradation signal are quantified by degradation rates and signal jumps in the degradation model. The uniqueness of our proposed approach is formulating the RUL prediction problem in a semi-Markov decision process framework, by which the system mean RUL can be obtained through the solution to a limited number of equations. To extend the use of our proposed approach in real applications, different failure standards according to different operational conditions are also considered. The application and effectiveness of this approach are illustrated by a turbofan engine dataset and a comparison with existing results for the same dataset.