基于随机过程的可靠性建模

基于Wiener过程、Gamma过程和逆高斯(Inverse Gaussian,IG)过程等随机过程的可靠性建模方法在装备可靠性评估中逐步得到应用.针对3类考虑随机影响的随机过程模型,分别阐述了各类模型在一元性能退化建模、二元性能退化建模、加速退化建模和竞争失效建模等情形下的应用情况,并展望了下一步研究方向.研究工作对...     

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.     

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。     

基于性能退化数据的可靠性评定方法研究

对于高可靠长寿命产品,基于性能退化数据分析产品可靠性是一种行之有效的技术途径。本文首先建立性能退化轨道模型和退化数据统计模型,然后详细论述了退化数据统计分析的多种大样本方法,并分析论述了各方法的优缺点。最后提出性能退化分析时,依然存在小样本问题,并提出性能退化轨道模型结合Bayes小样本技术,将是解决性能退化数据统计分析小样本问题的有效途径。     

基于带漂移的布朗运动的民用航空发动机实时性能可靠性预测

以民用航空发动机为研究对象,运用性能退化可靠性理论和随机过程方法,对发动机的可靠性进行了研究.通过分析发动机性能退化过程,建立了基于带漂移的布朗运动的可靠性模型,利用布朗运动特性研究了基于当前状态的退化时间预测方法,实例证明,该方法可操作性强,易于工程实现,为航空公司进行发动机机队科学管理提供了基础.      

Gamma过程退化失效可靠性分析

退化型失效产品,可通过退化失效分析对其进行可靠性统计推断。性能退化一般是一个随机过程,并且是单调变化的,为描述产品的退化,给出了一个随机Gamma过程模型,通过求解该过程的首达时分布即可获得产品的失效分布。由于解析方法求解存在很大的难度,给出了一种基于仿真的求解方法。利用所得模型对强激光装置所用的某型金属化膜脉冲电容器进行了可靠性分析,说明了模型的适用性。     

基于性能退化数据的液体火箭发动机可靠性Bayes评定

性能退化表征的是产品的工作能力随时间逐渐降低的现象。对于液体火箭发动机等试验费用昂贵的高可靠性产品,失效时间数据难以得到,此时性能退化数据是评定其可靠性的重要信息。文章提出用线性随机过程模型来描述产品性能参数逼近临界值的过程,再根据产品的失效机理,推导出产品的失效分布函数形式。相对于传统的寿命分布模型,文中推导的失效分布函数中的参数因物理含义明确而易于通过实时跟踪测量性能参数时间序列数据估计得到。文中还通过一个数值实例说明了该方法在工程上应用的有效性。     

动量轮维纳过程退化建模与寿命预测

针对动量轮寿命预测无失效数据问题,利用有漂移的维纳过程对其轴承温度建立性能退化模型,同时,考虑由于加工工艺、制造误差等因素所造成的不确定性,利用随机变量来描述其漂移参数和扩散参数,并结合自助法获取纠偏估计.根据所建模型仿真了轴承温度维纳过程的样本路径,最后,对动量轮进行随机失效阈值情形的寿命预测和剩余寿命预测.该模型为...     

基于退化模型的失效机理一致性检验方法

有效的加速试验必须保证产品的失效机理不变。针对加速退化试验(ADT)可靠性建模的特点,提出了一种基于退化模型的失效机理一致性检验方法。以基于随机过程和基于退化轨迹拟合的退化模型为例,通过加速系数的2种等效定义分别建立了模型参数与失效机理不变的内在联系,进一步推导出其参数在不同应力下须满足的关系。在合理假定检验样本服从正态分布的基础上,利用t统计量对样本的一致性进行检验。通过2个ADT实例验证了所提检验方法的合理性与可行性。     

Comparison of two reliability models for performance degradation analysis

In order to investigate the general reliability assessment methods based on performance degradation data, two commonly used stochastic process approaches, bilinear process method and random-effect model were studied. Analyzing procedure and effectiveness of these two methodologies were studied and compared. Meanwhile, the two approaches were illustrated through practical applications. The residual plots and the 10th percentile curves of the two methods were presented to demonstrate the comparative results. The random-effect model yielded more volatile residuals and a lower and unsafe 10th percentile curve. Consequently the bilinear process model can be concluded to derive more reasonable results due to its one-stage estimation property.     

A novel imprecise stochastic process model for time-variant or dynamic uncertainty quantification

This paper proposes a novel model named as “imprecise stochastic process model” to handle the dynamic uncertainty with insufficient sample information in real-world problems. In the imprecise stochastic process model, the imprecise probabilistic model rather than a precise probability distribution function is employed to characterize the uncertainty at each time point for a time-variant parameter, which provides an effective tool for problems with limited experimental samples. The linear correlation between variables at different time points for imprecise stochastic processes is described by defining the auto-correlation coefficient function and the cross-correlation coefficient function. For the convenience of analysis, this paper gives the definition of the P-box-based imprecise stochastic process and categorizes it into two classes: parameterized and non-parameterized P-box-based imprecise stochastic processes. Besides, a time-variant reliability analysis approach is developed based on the P-box-based imprecise stochastic process model, through which the interval of dynamic reliability for a structure under uncertain dynamic excitations or time-variant factors can be obtained. Finally, the effectiveness of the proposed method is verified by investigating three numerical examples.     

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.     

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。     

基于多性能参数的民用航空发动机 实时性能可靠性预测

以民用航空发动机为研究对象,运用性能退化可靠性理论,对发动机的性能可靠性进行了研究.通过分析发动机性能退化过程,利用状态空间方法建立了时变性能退化模型,并通过卡尔曼滤波对性能趋势进行预测;然后考虑各性能参数之间的相关性,运用随机过程理论建立了基于多性能参数的实时性能可靠性预测模型,从而对发动机的退化时间进行实时预测;最后通过实例证明该方法是有效的,并且易于工程实现,同时,也为航空公司进行发动机机队科学管理提供了基础.      

Application of the Kalman-Levy Filter for Tracking Maneuvering Targets

Among target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an interacting multiple model (IMM) estimator). The oversimplification resulting from the above assumptions can cause degradation in tracking performance. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. This filter assumes a heavy-tailed noise distribution known as the Levy distribution. Due to the heavy-tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of the Kalman-Levy filter in the nonmaneuvering portion of track is worse than that of a Kalman filter. For this reason, an IMM with one Kalman and one Kalman-Levy module is developed here. Also, the superiority of the IMM with Kalman-Levy module over only Kalman-filter-based IMM for realistic maneuvers is shown by simulation results.     

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。     

疲劳裂纹扩展随机模型

 应用随机理论与实验数据相结合的方法建立了疲劳裂纹扩展参量与时间关系的随机模型,根据随机模型建立了相应的随机微分方程,求解了有关的统计分布函数,并应用随机模拟方法再现了疲劳裂纹扩展的时间历程。     

Two new multi-phase reliability growth models from the perspective of time between failures and their applications

Aviation products would go through a multi-phase improvement in reliability performance during the research and development process. In the literature, most of the existing reliability growth models assume a constant failure intensity in each test phase, which inevitably limits the scope of the application. To address this problem, we propose two new models considering time-varying failure intensity in each stage. The proposed models borrow the idea from the accelerated failure-time models. It is assumed that time between failures follow the log-location-scale distribution and the scale parameters in each phase do not change, which forms the basis for integrating the data from all test stages. For the test-find-test scenario, an improvement factor is introduced to construct the relationship between two successive location parameters. Whereas for the test-fix-test scenario, the instantaneous cumulative time between failures is assumed to be consistent with Duane model and derive the formulation of location parameter. Likelihood ratio test is further utilized to test whether the assumption of constant failure intensity in each phase is suitable. Several applications with real reliability growth data show that the assumptions are reasonable and the proposed models outperform the existing models.     

基于特征参数趋势进化的故障诊断和预测方法

 采用时间序列方法对可以表征系统故障状态的特征参数的趋势进化进行预测,同时考虑特征参数的概率分布特性,给出了对系统进行故障诊断和预测的方法。在已获得特征参数监测数据的基础上,分别对具有广义强度/故障阈值确定分布或故障模式特征参数空间分布两种形式的故障判据,提出了利用二次指数平滑预测模型对系统未来某时刻的故障状态进行预测的方法。给出包括故障概率和故障指数在内的故障诊断和预测结果形式,可进一步为系统的维修决策等提供参考。