基于故障机理和伪失效寿命的 电子产品剩余寿命预测

针对传统电子产品可靠性评估中存在的不足,提出了一种基于故障机理和伪失效寿命的电子产品剩余寿命预测方法。首先,基于电子产品的故障模式、故障机理分析,确定产品敏感性能参数;然后,对敏感参数退化量进行监测,建立电子产品退化轨迹模型,利用最大似然法估计其参数;最后,根据电子产品退化轨迹,设定故障阈值,得到电子产品寿命分布。通过仿真表明,该方法评估精度较高,对电子产品的可靠性评估有一定的参考价值。     

航空磁探中水下目标三维积分方程磁场建模

为获取航空磁探中水下铁磁性目标的空间磁场分布,通过三维积分方程法的基本原理推导出空间磁场的解析式,根据矢量恒等式和高斯散度定理简化空间磁场计算式,建立水下铁磁性目标空间磁场预测模型。通过铁磁性长方体对模型进行理论验证,使用铯光泵磁力仪测量铁磁性长旋转椭球体的高空磁场;然后,基于预测模型推算铁磁性长旋转椭球体的磁场,根据实际测量的磁场数据和预测值进行比较。结果表明,预测模型的推算精度较高,平均绝对误差为0.320 5 nT,平均相对误差为12.368%。     

缺口件振动疲劳寿命预测的频域法和时域法的比较与分析

以缺口件为研究对象,在窄带信号下分别采用频域法和时域法预测缺口件的振动疲劳寿命。频域法选用Rayleigh模型和Dirlik模型作为应力变程概率密度p(S)模型,采用Miner累积损伤准则计算振动疲劳寿命。时域法通过傅里叶逆变换将结构危险点处的应力响应功率谱密度函数转为应力-时间历程样本,采用雨流循环计数结合Miner累积损伤准则,得到一个疲劳寿命样本,计算多个样本取均值作为振动疲劳寿命。计算结果和试验结果表明:时域法的精度较高,但工作量较大;选用 Rayieigh 模型和 Dirlik 模型的频域法预测寿命精度接近,比时域法的精度略差,但工作量较小。     

用遥测数据识别飞行外声场

给出了一种用遥测数据通过混响室噪声试验识别飞行外声场的方法,并提供了一个实例。该方法以遥测加速度功率谱为控制谱,以遥测点为响应测量点,通过噪声试验调节外声场声谱,使测量点的响应与控制谱一致,识别出飞行外声场。共识别了起飞段和跨音速段两种外声场。实例给出了某火箭某次遥测数据及识别外声场,并将识别外声场与该火箭另次飞行的实测外声场进行了比较。比较表明,识别外声场与实测外声场总声压级最大相差5.2dB,谱型振动能量分布存在较大差别。作为有限条件下(仅有遥测数据)获取外声场的一种方法,并以此外声场作为输入载荷对试件进行故障分析和振动环境获取,从飞行结果看,该方法是可行有效的。     

流量预测下智能寻优门限值的AOS帧生成算法

针对AOS空间通信系统的自适应帧生成算法帧生成时间门限值需要人工设置问题,提出一种流量预测下智能寻优门限值的AOS帧生成算法.该算法使用小波神经网络对ON/OFF模型产生的自相似流量进行预测,根据预测流量计算复用效率和平均包时延,使用人工鱼群算法对建立的综合评价函数进行智能寻优,从而得到帧生成算法的最佳门限值.该算法可...     

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。     

Development cost prediction of general aviation aircraft projects with parametric modeling

The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.     

Analysis of GNSS clock prediction performance with different interrupt intervals and application to real-time kinematic precise point positioning

Continuous and timely real-time satellite orbit and clock products are mandatory for real-time precise point positioning (RT-PPP). Real-time high-precision satellite orbit and clock products should be predicted within a short time in case of communication delay or connection breakdown in practical applications. For prediction, historical data describing the characteristics of the real-time orbit and clock can be used as the basis for performing the prediction. When historical data are scarce, it is difficult for many existing models to perform precise predictions. In this paper, a linear regression model is used to predict clock products. Seven-day GeoForschungsZentrum (GFZ) final clock products sampled at 30 s are used to analyze the characteristics of GNSS clocks. It is shown that the linear regression model can be used as the prediction model for the satellite clock products. In addition, the accuracy of the clock prediction for different satellites are analyzed using historical data with different periods (such as 2 and 10 epochs). Experimental results show that the accuracy of the clock with the linear regression prediction model using historical data with 10 epochs is 1.0 ns within 900 s. This is higher accuracy than that achieved using historical data of 2 epochs. Finally, the performance analysis for real-time kinematic precise point positioning (PPP) is provided using GFZ final clock prediction results and state space representation (SSR) clock prediction results when communication delay or connection breakdown occur. Experimental results show that the positioning accuracy without prediction is better than that with prediction in general, whether using the final clock product or the SSR clock product. For the final clock product, the positioning accuracy in the north (N), east (E), and up (U) directions is better than 10.0 cm with all visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 15.0 cm to 7.0 cm. For the SSR clock product, the positioning accuracy of N, E, and U directions is better than 12.0 cm with visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible GNSS satellites whose prediction accuracy is better than 0.1 ns using historical data of 10 epochs is improved from 12.0 cm to 9.0 cm.     

Effects of splined shaft bending rigidity on cylinder tilt behaviour for high-speed electro-hydrostatic actuator pumps

High-speed axial piston pumps are hydraulic power supplies for electro-hydrostatic actuators(EHAs). The efficiency of a pump directly affects the operating performance of an EHA, and an understanding of the physical phenomena occurring in the cylinder/valve plate interface is essential to investigate energy dissipation. The effects of the splined shaft bending rigidity on the cylinder tilt behaviour in an EHA pump need to be considered, because the deflection and radial expansion of a steel shaft rotating at a high speed cannot be ignored. This paper proposes a new mathematical model to predict the cylinder tilt behaviour by establishing a quantitative relationship between the splined shaft deflection, the cylinder tilt angle, and the tilt azimuth angle. The moments exerted by the splined shaft are included in the equilibrium equation of the cylinder. The effects of solid and hollow splined shafts equipped in an EHA pump prototype are compared at variable speeds of 5000–10,000 r/min. With a weight saving of 29.7%, the hollow shaft is experimentally found to have almost no influence on the volumetric efficiency, but to reduce the mechanical efficiency by 0.6–2.4%. The results agree with the trivial differences of the simulated central gap heights of the interface between the two shafts and the enlargement of the simulated tilt angles by the hollow shaft. The findings could guide designs of the cylinder/valve plate interface and the splined shaft to improve both the efficiency and power density of an EHA pump.     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).