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.     

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.     

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 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。     

Erosion degradation characteristics of a linear electro-hydrostatic actuator under a high-frequency turbulent flow field

The paper proposes a performance degradation analysis model based on dynamic erosion wear for a novel Linear Electro-Hydrostatic Actuator (LEHA). Rather than the traditional statistical methods based on degradation data, the method proposed in this paper firstly analyzes the dominant progressive failure mode of the LEHA based on the working principle and working conditions of the LEHA. The Computational Fluid Dynamics (CFD) method, combining the turbulent theory and the micro erosion principle, is used to establish an erosion model of the rectification mechanism. The erosion rates for different port openings, under a time-varying flow field, are obtained. The piecewise linearization method is applied to update the concentration of contaminated particles within the LEHA, in order to gain insight into the erosion degradation process at various stages of degradation. The main contribution of the proposed model is the application of the dynamic concentration of contamination particles in erosion analysis of Electro-Hydraulic Servo Valves (EHSVs), throttle valves, spool valves, and needle valves. The effects of system parameters and working conditions on component wear are analyzed by simulations. The results of the proposed model match the expected degradation process.     

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。     

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.     

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 GPA diagnostic system foraeroengine applications

In this research, a GPA(Gas Path Analysis) diagnostic system enhanced with GPA Index is described for gas path sensor and component fault diagnosis.A method of measurement correction is used in order that the measurement data obtained at un-standard ambient and operating conditions can be used for diagnostic analysis.The developed diagnostic system has been implemented into a Cranfield University gas turbine performance and diagnostic analysis software PYTHIA for gas turbine performance degradation analysis.The developed method and software have been applied to a model aero gas turbine engine to test the effectiveness of the system.The analysis shows that the developed diagnostic system can diagnose degraded sensor and components effectively using performance deviation measured at un-standard ambient and operational conditions.Theoretically, the idea of the diagnostic approach can be applied to different gas turbine engines.      

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。      

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.     

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.     

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。     

飞机故障的运行后果及成本分析方法

系统故障的运行后果分析技术是飞机运行可靠性分析的重要分支,可为维修任务的成本效益分析提供有力支撑。为了研究飞机系统故障对运行后果及成本的影响,从系统故障运行后果的相关性分析入手,首先,梳理飞机系统故障类型和放行情况对运行后果的影响;然后,结合航线维修现行做法及事件树思想提出一种支持评估飞机系统故障的运行后果及其相关成本的方法,建立基于飞机系统故障的运行后果与成本评估模型;最后,以某型号飞机燃油系统为例,验证所提方法的可行性和适用性。结果表明：本文所提方法合理有效,可为评估故障运行后果提供支持,为飞机的设计、运行和维护提供一定的参考。     

采用一组卡尔曼滤波器检测发动机传感器故障

在发动机全功能数字电子控制系统中,提高传感器工作的可靠性是十分重要的,除了不断对传感器本身的性能加以改进提高外,现在广泛地采用了余度技术。近二十年来对解析余度(Analyt ical Redundancy)进行了广泛的研究,解析余度(AR)方法是基于各状态变量之间存在的解析关系,在系统可观条件下,利用无故障的输出测量值去估计(构造)已故障传感器正常工作状态时的输出信息,从而实现对故障的检测、隔离与重构,保证控制系统具有预定的控制性能。      

Prognostic Health Management of Aircraft Power Generators

In this paper, prognostic tools are developed to detect the onset of electrical failures in an aircraft power generator, and to predict the generator's remaining useful life (RUL). Focus is on the rotor circuit since failure mode, effects, and criticality analysis (FMECA) studies indicate that it is a high priority candidate for condition monitoring. A signature feature is developed and tested by seeded fault experiments to verify that the initial stages of rotor faults are observable under diverse generator load conditions. A tracking filter is used to assess the damage state and predict generator RUL. This information helps to avoid unexpected failures while reducing the overall life-cycle cost of the system.     

Aircraft air conditioning system health state estimation and prediction for predictive maintenance

The vast potential of system health monitoring and condition based maintenance on modern commercial aircraft is being realized through the innovative use of Airplane Condition Monitoring System (ACMS) data. However there are few methods addressing the issues of failure prognostics and predictive maintenance for commercial aircraft Air Conditioning System (ACS). This study developed a Bayesian failure prognostics approach using ACMS data for predictive maintenance of ACS. First, a health index characterizing the ACS health state is inferred from a multiple sensor signals using a data driven method. Then a dynamic linear model is proposed to describe the degradation process for failure prognostics. Bayesian inference formulas are carried out for degradation estimation and prediction. The developed approach is applied on a passenger aircraft fleet with ACMS data recorded for one year. The analysis of the case study shows that the developed method can produce satisfactory prognostics results, where all the ACS failure precursors are identified in advance, and the relative errors for the failure time prediction made when just entering the degradation warning stage are less than 8%. This would allow operators to proactively plan future maintenance.     

基于大数据的民用飞机未来运营模式探索

大数据作为一种新兴的IT实现方式,在深刻影响IT业变革的同时,也为航空业带来了新的发展机遇。飞机运行过程中产生的海量运营数据最能体现民机的大数据基因,基于大数据的飞机运营支援将会是其在航空产业施展拳脚的主要战场之一。首先通过对航空大数据的梳理与分析,获得运营数据的种类、结构、数据流程以及相关特点。然后结合航空公司的具体业务需求,针对飞机典型复杂系统,分别以健康监控中的故障诊断、性能退化预测以及维修辅助决策几大关键技术作为大数据应用的切入点介绍技术研究路线,利用数据挖掘、机器学习等方法识别运营数据在系统不同健康状态下的特征表达,准确预测系统退化趋势,设置风险预警模型以提前预知潜在故障,最后从工程应用角度探讨这些关键技术的大数据实现平台以及基于大数据的运营业务流程。