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。     

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。     

Coupled evolution of piston asperity and cylinder bore contour of piston/cylinder pair in axial piston pump

The wear condition of the piston/cylinder pair is crucial to the performance and reliability of the axial piston pump. The hard piston surface, the soft cylinder bore surface, and the interface oil film affects each other during the wear process. Specifically, in the mixed lubrication region, the geometry of the hard piston surface asperity directly affects the wear of soft cylinder bore surface, while the asperities may deform or even degrade when penetrating and sliding against the cylinder bore. So far, there is no suitable method to simulate their coupled evolution. This paper proposed a wear process simulation model considering the real-time interaction between the elasto-plastic deformation of the piston surface asperity, the wear contour of the cylinder bore, and the lubrication condition of the interface. An offline library of the elasto-plastic constitutive behavior of the asperity based on the finite element method (FEM) is established as a part of the simulation model to precisely analyze the deformation and degradation of the asperity and quickly invoke them in the numerical wear process simulation. The simulation and experimental results show that the piston asperity and the cylinder bore contour converge to a steady state after running-in for about 0.5 h. The distribution of the simulated asperity degradation and wear depth is also verified by the experiment.     

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.     

A new remaining useful life estimation method for equipment subjected to intervention of imperfect maintenance activities

As the key part of Prognostics and Health Management (PHM), Remaining Useful Life (RUL) estimation has been extensively investigated in recent years. Current RUL estimation studies considering the intervention of imperfect maintenance activities usually assumed that maintenance activities have a single influence on the degradation level or degradation rate, but not on both. Aimed at this problem, this paper proposes a new degradation modeling and RUL estimation method taking the influence of imperfect maintenance activities on both the degradation level and the degradation rate into account. Toward this end, a stochastic degradation model considering imperfect maintenance activities is firstly constructed based on the diffusion process. Then, the Probability Density Function (PDF) of the RUL is derived by the convolution operator under the concept of First Hitting Time (FHT). To implement the proposed RUL estimation method, the Maximum Likelihood Estimation (MLE) is utilized to estimate the degradation related parameters based on the Condition Monitoring (CM) data, while the Bayesian method is utilized to estimate the maintenance related parameters based on the maintenance data. Finally, a numerical example and a practical case study are provided to demonstrate the superiority of the proposed method. The experimental results show that the proposed method could greatly improve the RUL estimation accuracy for the degrading equipment subjected to imperfect maintenance activities.     

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。      

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。     

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。     

轴向柱塞泵滑靴副的热结构耦合特性

为了改善轴向柱塞泵滑靴副的耐磨损性能,建立了滑靴与斜盘摩擦副的瞬态热结构耦合模型,分析压力冲击条件下滑靴的表面温度、应力以及变形的变化规律.研究结果表明:某型轴向柱塞泵中滑靴温度随柱塞腔压力呈周期性变化,滑靴温度范围为45.5~49.8℃,且滑靴的最高温度出现在泵的吸排油过渡区.当滑靴处于泵的排油区时,滑靴的最大轴向应力为250MPa,集中在滑靴油腔与密封带之间的边缘区域.滑靴的轴向应力分层显著,引起滑靴的变形分化,其变形量为12.5~15μm,出现在滑靴的边缘.由于滑靴的输入热流密度增强磨粒的剪切力,加剧滑靴表面的微切削和挤压变形,导致滑靴表面出现条状剥落和凹坑磨损,呈现出黏着和磨粒磨损特征.      

柱塞倾角对航空柱塞泵性能的影响分析

通过对柱塞泵中柱塞的运动方程和柱塞泵的瞬时供油量方程进行分析,利用AMEsim工程仿真软件,建立了某型航空发动机柱塞泵的仿真模型,通过仿真分析了柱塞倾角对柱塞行程、柱塞泵的流量脉动和压力脉动的影响,仿真结果表明柱塞倾角是影响泵流量脉动的重要因素,适当增大柱塞倾角,可以增大柱塞的行程,增加泵的供油量,还能够改善柱塞泵流量的脉动。     

轴向柱塞泵多学科融合建模与集成优化

为了实现机液耦合条件下轴向柱塞泵的精确建模和优化设计,提出了轴向柱塞泵多学科融合建模与集成优化方法。以国产I3V2-10S型轴向柱塞泵为对象,分别建立了ADAMS机械系统模型和AMESIM液压系统模型,通过ADAMS和AMESIM之间的接口模块设计实现了柱塞泵机液耦合建模和联合仿真,并利用iSIGHT软件集成了该机液耦合模型,以该柱塞泵出口体积流量脉动率最小为目标,对配流副中吸排油节流口最大开口等效直径、吸排油闭死角、柱塞包角、吸排油预开口量等参数进行了优化设计。结果表明:在负载工况分别为10、14、18、22、26、30MPa时,优化后的泵出口流量脉动率比优化前分别降低了14.59%、18.57%、21.50%、23.44%、24.03%、25.49%,最后通过实验验证了仿真和集成优化数据的正确性。      

基于非线性Wiener过程航空发动机性能退化预测

针对线性随机过程航空发动机剩余使用寿命预测精度不高的问题,提出一种漂移系数为指数形式的非线性Wiener过程发动机性能退化建模,进而预测航空发动机的剩余寿命。基于直接监测发动机性能退化数据,构建发动机性能退化模型,根据Wiener过程首达阈值时间的数学性质,推导出剩余寿命的概率分布。通过极大似然估计构建退化模型中未知参数的似然函数,利用遗传算法得到发动机总体模型参数的离线估计值。考虑到不同发动机个体间的差异性,采用贝叶斯公式,结合发动机的实时监测数据与总体模型参数的先验分布对模型中随机参数进行实时更新,从而对个体发动机的剩余寿命实时预测。最后,选择商用航空发动机仿真数据集(C-MAPSS)进行实验,结果表明：针对个体发动机基于非线性随机过程方法,实时更新非线性Wiener方法能够提高航空发动机循环中期剩余寿命预测的准确性,提供更加可靠的预防性维修决策。     

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.     

二维活塞航空燃油泵容积效率分析

提出了一种二维活塞燃油泵，该泵利用二维活塞的旋转运动将配流机构集成到活塞上，去除了传统柱塞泵独立的配流机构，简化了泵的结构、提升了泵的功率密度，而且由于主要运动机构采用了滚动轴承支撑，免除了滑动摩擦副及其产生的泄漏，提高了效率。对泵的容积效率进行理论分析和实验研究。结合泵的结构原理分析了造成容积损失的内泄、外泄及油液压缩性等3种主要原因；应用层流和紊流的缝隙流动基本理论推导出泄漏解析表达式，着重分析了不同配流开口形式产生的内泄漏；综合考虑油液压缩性和泄漏获得了容积效率数学模型。理论研究表明采用负开口配流形式可以有效减小内泄漏，定量分析了泄漏流量与负开口量的关系，同时利用Fluent进行配流过程中泵腔内的流场数值模拟，结果表明可以有效减小油液倒灌。为了验证理论分析的有效性，制作二维活塞燃油泵样机，以航空煤油为介质进行台架实验。样机实验结果表明在负载压力2 MPa，转速从1 000 r/min到5 000 r/min，容积效率从93.6%提升到98.1%；当转速为2 000 r/min，负载压力从1 MPa增大到5 MPa，容积效率从97.5%降低到92.3%。以现有的齿轮泵和柱塞泵相比容积效率显著提高，理论与实验的结果偏差在4%以内，表明理论分析的有效性和正确性。     

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.     

Reliability estimation of mechanical seals based on bivariate dependence analysis and considering model uncertainty

The reliability estimation of mechanical seals is of crucial importance due to their wide applications in pumps in various mechanical systems. Failure of mechanical seals might cause leakage, and might lead to system failure and other relevant consequences. In this study, the reliability estimation for mechanical seals based on bivariate dependence analysis and considering model uncertainty is proposed. The friction torque and leakage rate are two degradation performance indicators of mechanical seals that can be described by the Wiener process, Gamma process, and inverse Gaussian process. The dependence between the two indicators can be described by different copula functions. Then the model uncertainty is considered in the reliability estimation using the Bayesian Model Average (BMA) method, while the unknown parameters in the model are estimated by Bayesian Markov Chain Monte Carlo (MCMC) method. A numerical simulation study and fatigue crack study are conducted to demonstrate the effectiveness of the BMA method to capture model uncertainty. A degradation test of mechanical seals is conducted to verify the proposed model. The optimal stochastic process models for two performance indicators and copula function are determined based on the degradation data. The results show the necessity of using the BMA method in degradation modeling.     

二维燃油泵的设计与研究

提出一种新型的二维燃油泵，说明了其诸多优点。为研究其结构性流量脉动，利用仿真软件AMESIM建立了二维燃油泵的仿真模型进行理论研究，并进行了实验验证。通过齿轮泵与轴向柱塞泵的对比，说明了轴向柱塞泵在燃油系统应用中的优势。针对轴向柱塞泵中存在的一些不可避免的缺陷，提出了能避免这些缺陷的二维燃油泵。通过解释二维燃油泵的结构组成与工作原理，说明了二维燃油泵的优势，包括结构力平衡、工作效率高、无直接的滑动摩擦副和无结构性流量脉动等。为了解释无结构性流量脉动这一优点，进行了仿真分析与实验验证，验证了二维燃油泵具有无结构性流量脉动这一潜力。     

柱塞泵热力学建模与仿真

针对航空行业广泛应用的柱塞泵的热力学特性,提出了一种分析和建模的方法.应用能量守恒定律推导并建立了集中参数的航空柱塞泵的热力学模型,并针对柱塞泵的内部结构进行了较详细的传热分析.将此建模和分析的方法应用于包含航空柱塞泵的一个典型液压回路.通过对航空柱塞泵的热力学实验与仿真结果的比较证明了建模方法的有效性.     

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.