Reliability assessment of engine electronic controllers based on Bayesian deep learning and cloud computing

The reliability of an Engine Electronic Controller (EEC) attracts attention, which has a critical impact on aircraft engine safety. Reliability assessment is an important part of the design phase. However, the complex composition of EEC and the characteristic of the Phased-Mission System (PMS) lead to the difficulty of assessment. This paper puts forward an advanced approach, considering the complex products and uncertain mission profiles to evaluate the Mean Time Between Failures (MTBF) in the design phase. The failure mechanisms of complex components are deduced by Bayesian Deep Learning (BDL) intelligent algorithm. And copious samples of reliability simulation are solved by cloud computing technology. Based on the result of BDL and cloud computing, simulations are conducted with the Physics of Failure (PoF) theory and Failure Behavior Model (FBM). This reliability assessment approach can evaluate MTBF of electronic products without reference to physical tests. Finally, an EEC is applied to verify the effectiveness and accuracy of the method.     

Using PoF models to predict system reliability considering failure collaboration

Existing Physics-of-Failure-based (PoF-based) system reliability prediction methods are grounded on the independence assumption, which overlooks the dependency among the compo-nents. In this paper, a new type of dependency, referred to as failure collaboration, is introduced and considered in reliability predictions. A PoF-based model is developed to describe the failure behavior of systems subject to failure collaboration. Based on the developed model, the Bisection-based Reliability Analysis Method (BRAM) is exploited to calculate the system reliabil-ity. The developed methods are applied to predicting the reliability of a Hydraulic Servo Actuator (HSA). The results demonstrate that the developed methods outperform the traditional PoF-based reliability prediction methods when applied to systems subject to failure collaboration.     

Failure behavior analysis of phased-mission systems considering functional and physical isolation effects

Phased-Mission Systems(PMS) are widely applied in aerospace, telecommunication and intelligent systems for multiple, consecutive and non-overlapping phases of missions. The phasedependent stresses and system structure cause some difficulties to the reliability analysis of PMSs.In this paper, we analyze the physical isolation effects on the degradation speeds and across-phase damage accumulations of failure mechanisms. And, some corresponding reliability and unreliability formulas are derived. Be...     

Macro-phenomenological high-strain-rate elastic fracture model for ice-impact simulations

The ice impact can cause a severe damage to an aircraft’s exposed structure, thus, requiring its prevention. The numerical simulation represents an effective method to overcome this challenge. The establishment of the ice material model is critical. However, ice is not a common structural material and exhibits an extremely complex material behavior. The material models of ice reported so far are not able to accurately simulate the ice behavior at high strain rates. This study proposes a novel high-precision macro-phenomenological elastic fracture model based on the brittle behavior of ice at high strain rates. The developed model has been compared with five reported models by using the smoothed particle hydrodynamics method so as to simulate the ice-impact process with respect to the impact speeds and ice shapes. The important metrics and phenomena (impact force history, deformation and fragmentation of the ice projectile and deflection of the target) were compared with the experimental data reported in the literature. The findings obtained from the developed model are observed to be most consistent with the experimental data, which demonstrates that the model represents the basic physics and phenomena governing the ice impact at high strain rates. The developed model includes a relatively fewer number of material parameters. Further, the used parameters have a clear physical meaning and can be directly obtained through experiments. Moreover, no adjustment of any material parameter is needed, and the consumption duration is also acceptable. These advantages indicate that the developed model is suitable for simulating the ice-impact process and can be applied for the anti-ice impact design in aviation.     

纤维缠绕复合材料结构可靠性评估方法及其应用

为了更加科学有效地评估纤维缠绕复合材料结构的可靠性,根据复合材料渐进失效特点,应用结构系统可靠性理论和方法,将复合材料视为连续体的结构系统,基于复合材料有限元模型对"单元"和"系统"进行了定义,构建了概率渐进失效分析的基本流程,提出了主要失效序列和结构整体的失效概率的精确解算式和近似计算方法.为解决复杂结构系统主要失效...     

An optimization method for metamorphic mechanisms based on multidisciplinary design optimization

The optimization of metamorphic mechanisms is different from that of the conventional mechanisms for its characteristics of multi-configuration. There exist complex coupled design variables and constraints in its multiple different configuration optimization models. To achieve the compatible optimized results of these coupled design variables, an optimization method for metamorphic mechanisms is developed in the paper based on the principle of multidisciplinary design optimization（MDO）. Firstly, the optimization characteristics of the metamorphic mechanism are summarized distinctly by proposing the classification of design variables and constraints as well as coupling interactions among its different configuration optimization models. Further, collaborative optimization technique which is used in MDO is adopted for achieving the overall optimization performance. The whole optimization process is then proposed by constructing a two-level hierarchical scheme with global optimizer and configuration optimizer loops. The method is demonstrated by optimizing a planar five-bar metamorphic mechanism which has two configurations,and results show that it can achieve coordinated optimization results for the same parameters in different configuration optimization models.     

功能分析与失效物理结合的可靠性预计方法

 可靠性预计是产品设计、研发过程中的重要工作,全面准确的可靠性预计可以评价产品的可靠性水平,也可以为设计提供信息,指导设计。全面分析总结当前电子设备可靠性预计相关技术方法,以当前基于失效物理(POF)技术的系统可靠性预计方法中,并未考虑产品功能组成关系的缺陷为突破点,建立了一种以失效物理分析为基础,综合考虑电路功能组成关系的电子设备可靠性预计方法。该方法从电路功能出发,通过灵敏度仿真和主成分分析两种方法,确定对电路性能起主要影响的关键单元,再通过失效物理分析或统计规律明确单元的失效概率分布,通过混合分布获得系统的分布,得到系统可靠性指标。最后以某航空机电产品的电源电路为案例,对本预计方法进行验证。     

电子整机加速贮存试验的Dirichlet分析方法

针对电子整机系统结构复杂,失效机理众多,无法利用传统的加速模型外推对其寿命和可靠性特征进行分析的问题,提出一种基于顺序Dirichlet分布的分析模型,利用多应力、多水平的环境应力,对每一阶段上的失效率建立指数分布模型。通过先验信息和基于反应论的修正加速模型,给出各应力水平上的失效率先验信息,利用多变量顺序Dirichlet分布描述先验失效率概率密度函数,并根据先验信息对Dirichlet分布参数进行辨识设计和对参数物理意义进行阐述。根据恒加定数试验特点,提出似然函数的解析步骤,利用Gibbs拒绝抽样方法对Dirichlet后验分布进行推断分析,得到后验信息。最后分析一个实例,给出抽样过程和几个分位点上的失效率估计值,并比较正常状态下先验和后验的可靠度变化趋势,验证算法具有一定的效率,为电子整机寿命预测与评估提供一种新方法。     

守恒型可压缩一维湍流方法及其在超声速标量混合层中的应用

一维湍流（ODT）方法是一种能在一维计算域上遵循湍流基本物理规律的湍流建模方法。通过结合确定性和随机性求解方法,能够在一维计算域上准确捕捉到湍流统计规律,且降维建模可显著减小计算量。ODT方法主要被广泛用于不可压湍流和湍流燃烧研究,若要将其拓展用于模拟高速可压缩湍流,需对建模方法进行深度改进。相比于不可压ODT方法,本文基于欧拉参考框架,针对可压缩湍流的特性,将因变量由原始变量改为有利于减小可压缩湍流模拟误差的守恒通量,并加入了组分求解模块。对确定性和随机性求解模块均进行了相应的深度改进,开发出具有标量混合模拟功能的守恒型可压缩ODT方法。在确定性模块中改为求解以守恒通量为变量的一维截断控制方程,在随机性模块中构造一维涡时,将三联映射的作用对象也相应地由原始变量改为守恒通量,并选用了可保证变密度情况下动量守恒的双核变换。通过模拟空间发展超声速平面湍流混合层并将自相似阶段结果与实验结果比对,验证该方法对可压缩剪切湍流场中标量混合的捕捉精度。守恒型可压缩ODT方法模拟得到的速度场和组分场的平均剖面和脉动强度分布与实验结果准确吻合,精度明显优于传统的耦合梯度扩散亚格子模型的大涡模拟方法（LES-GRAD.DIFF.）以及耦合线性涡（LEM）亚格子模型的大涡模拟方法（LES-LEM）,且该方法的降维处理使其在降低计算成本方面具有显著优势。     

基于失效概率的涡轮后机匣全局灵敏度分析

涡轮后机匣是航空发动机安全的关键部件,但是其具有工况复杂、不确定性因素多的缺点。为了探究输入随机变量的不确定性对涡轮后机匣结构失效概率的影响,建立参数化有限元模型进行确定性分析。考虑材料性能、几何参数及外部载荷的不确定性,对涡轮后机匣两种典型失效模式：强度失效以及刚度失效建立极限状态函数;通过构造自适应Kriging 代理模型并结合重要抽样方法评估涡轮后机匣结构失效概率,利用基于失效概率的全局灵敏度方法对涡轮后机匣结构可靠度的不确定性来源进行分析,对各输入随机变量重要性进行排序,构建一种涡轮后机匣全局灵敏度分析框架。结果表明：涡轮后机匣在两种失效模式以及系统失效模式下,发动机推力以及线性膨胀系数对结构失效概率影响最为显著,应对其重点考虑;内外机匣长度以及材料弹性模量对涡轮后机匣结构失效概率影响较小,可对其适当忽略。     

Application of the Volterra Series to the Analysis and Design of an Angle Track Loop

A typical function of an angle tracking loop is to keep a radar antenna pointed at a target. The error in pointing is directly related to successful operation of the tracking device; therefore, its behavior is of interest. For a tracker with a general polynomial nonlinearity, an arbitrary initial pointing error, and a bounded deterministic input, a method is developed for finding upper bounds on the magnitude of the tracking error using Volterra series techniques. Convergence regions of the Volterra series are also obtained. Applications of these results are made to a second-order tracking device.     

A novel method for importance measure analysis in the presence of epistemic and aleatory uncertainties

For structural systems with both epistemic and aleatory uncertainties, research on quantifying the contribution of the epistemic and aleatory uncertainties to the failure probability of the systems is conducted. Based on the method of separating epistemic and aleatory uncertainties in a variable, the core idea of the research is firstly to establish a novel deterministic transition model for auxiliary variables, distribution parameters, random variables, failure probability, then to propose the improved importance sampling(IS) to solve the transition model. Furthermore,the distribution parameters and auxiliary variables are sampled simultaneously and independently;therefore, the inefficient sampling procedure with an ‘‘inner-loop' for epistemic uncertainty and an‘‘outer-loop' for aleatory uncertainty in traditional methods is avoided. Since the proposed method combines the fast convergence of the proper estimates and searches failure samples in the interesting regions with high efficiency, the proposed method is more efficient than traditional methods for the variance-based failure probability sensitivity measures in the presence of epistemic and aleatory uncertainties. Two numerical examples and one engineering example are introduced for demonstrating the efficiency and precision of the proposed method for structural systems with both epistemic and aleatory uncertainties.     

基于故障物理的电路板可靠性分析

在装备生命周期的各阶段,其承受的环境载荷都可能对装备造成累积损伤,进而引发故障。从装备设计特性和寿命期环境应力分布及其相互作用角度来分析装备的故障更符合其未来实际使用情况。介绍了电路板常用的故障物理模型,阐述了基于故障物理的电路板可靠性分析流程,明确了潜在故障仿真思路,为装备早期可靠性设计、测试性设计与评价提供了方法依据。     

An analysis and enhanced proposal of atmospheric boundary layer wind modelling techniques for automation of air traffic management

The air traffic management automation imposes stringent requirements on the weather models, in such a way that they should be able to provide reliable short-time forecasts in digital formats in almost real time. The atmospheric boundary layer is one of the regions where aircraft operation and coordination are critical and therefore atmospheric model performance is also vital. This paper presents conventional and innovative techniques to improve the accuracy in the forecasting of winds in the lower atmospheric layer, proposing mechanisms to develop better models including deterministic and stochastic simulations. Accuracy is improved by optimizing the grid, assimilating observations in cycling simulations and managing a number of ensemble members. An operation-driven post-processing stage helps to incorporate detailed terrain definitions and real-time observations without re-running the model. The improvements are checked against mesoscale weather simulations at different scales and a dedicated flight campaign. The results show good performance of the model without sensitively increasing the required throughput.     

Iterative MMSE method and recurrent Kalman procedure for ISAR imagereconstruction

This work presents a novel approximate iterative and recurrent approach for image reconstruction from inverse synthetic aperture radar (ISAR) data. Mathematical models of the quadrature components of the ISAR signal, reflected by an object with a complex geometry, are devised. Approximation matrix functions are used to describe deterministic signals reflected by point scatterers located at nodes of the uniform grid (model) during inverse aperture synthesis. Minimum mean square error (MMSE) equations and Kalman equations are derived. To prove the validity and correctness of the developed iterative MMSE method and recurrent Kalman procedure, numerical experiments were performed. The computational results demonstrate high resolution images, unambiguous and convergent estimates of the point scatterers' intensities of a target from simulated ISAR data     

Methods to determine stress profile in ALT based on theoretical life models

With the recent products being more reliable,engineers cannot obtain enough failure or degradation information through the design period and even the product lifetime,therefore,accelerated life test(ALT)has become the most popular way to quantify the life characteristics of products.Test design is the most essential topic,such as testing duration,stress profile,data inference,etc.In this paper,a method and procedure based on theoretical life models is proposed to determine the accelerated stress profile.Firstly,the method for theoretical life calculation is put forward based on the main failure mechanism analysis and the theoretical life models.Secondly,the method is provided to determine the accelerated stress profile,including the method to determine the accelerated stress types and the stress range on the basis of the main failure mechanism analysis,the method to determine the acceleration factor and the accelerated stress level based on life quantitative calculation models,and the collaborative analysis method of the accelerated test time while taking the multiple failure mechanisms into consideration.Lastly,the actuator is taken as an example to describe the procedure of the method and the engineering applicability and the validity are verified.     

随机环境应力冲击下基于多参数相关退化的导弹部件寿命预测

为了解决某型导弹部件的贮存寿命预测问题,提出了一种随机环境应力冲击下基于多参数相关退化的寿命预测方法。针对产品存在退化失效与突发失效两种失效模式,利用Wiener、Gamma及Inverse Gaussian等随机过程模型拟合各性能参数的退化数据,并采用Copula函数进行相关性退化失效建模;利用随机环境应力冲击解释突发失效的机理,并采用非均匀泊松过程对突发失效建模;进而建立退化失效与突发失效竞争的贮存寿命预测模型。实例应用说明所提方法能够反映出导弹部件的失效规律,比传统预测方法具有更高的预测准确性,具有较好的工程应用价值。     

驾驶员模糊控制模型与仿真分析

根据飞机驾驶员的控制行为，建立了驾驶员的基本模糊控制模型，然后利用李雅普洛夫综合法设计了两种自适应算法，来调整基本模糊控制模型的某些参数，建立了两类自适应模糊控制模型。对风切变大气环境下飞机进场下滑过程进行了仿真计算，结果表明，所建立的模型是合理的，能在一定程度上反映驾驶员的操纵情况。     

Multiscale modeling of mechanical behavior and failure mechanism of 3D angle-interlock woven aluminum composites subjected to warp/weft directional tension loading

The mechanical behavior and progressive damage mechanism of novel aluminum matrix composites reinforced with 3D angle-interlock woven carbon fibers were investigated using a multiscale modeling approach. The mechanical properties and failure of yarns were evaluated using a microscale model under different loading scenarios. On this basis, a mesoscale model was developed to analyze the tensile behavior and failure mechanism of the composites. The interfacial decohesion, matrix damage, and failure of fibers and yarns were incorporated into the microscopic and mesoscopic models. The stress–strain curves and fracture modes from simulation show good agreement with the experimental curves and fracture morphology. Local interface and matrix damage initiate first under warp directional tension. Thereafter, interfacial failure, weft yarn cracking, and matrix failure occur successively. Axial fracture of warp yarn, which displays a quasi-ductile fracture characteristic, dominates the ultimate composites failure. Under weft directional tension, interfacial failure and warp yarn rupture occur at the early and middle stages. Matrix failure and weft yarn fracture emerge simultaneously at the final stage, leading to the cata-strophic failure of composites. The weft directional strength and fracture strain are lower than the warp directional ones because of the lower weft density and the more serious brittle fracture of weft yarns.     

基于神经网络的故障率预测方法

 为了更好地预测产品故障率,提出了基于神经网络的故障率预测方法,分别给出了基于反向传播(BP)网络和径向基函数(RBF)网络进行故障率预测的基本思想、预测模型和实施步骤。分别对比分析了神经网络法与回归分析法、分解分析法、移动平均法、指数平滑法、自适应过滤法、自回归移动平均混合(ARMA)模型等统计预测方法的区别,对照故障率的特点,说明了神经网络法是其中最适用于故障率预测的统计方法。最后分别按这两种模型对某航空公司波音飞机故障率进行了预测,预测结果表明：这两种模型均适用于故障率预测,预测值与真实值的误差在20%之内,且RBF网络的预测效果略优于BP网络,此外通过与上述统计预测法的误差进行对比,说明神经网络法预测误差最小。