一种基于OPTICS聚类的仅测角导航目标检测算法

仅测角自主导航方法具有设备简单、复杂度低，功耗低的优点，在空间任务中具有广泛的应用前景.针对中远距离下空间目标特征少的特点，提出了一种利用基于OPTICS聚类算法的空间目标检测方法，可用于仅测角导航过程中的目标检测.对原始星图进行预处理提取星点及目标点，并结合星图识别的结果选择部分帧，使用经过改进的OPTICS聚类方法获得目标运动轨迹.最后，使用本文中的算法对软件仿真出的含有目标的高精度星图进行处理验证了算法的可行性.在卫星相对于空间目标抵近过程中，目标检测的水平误差及垂直误差小于0.15°的帧数分别占到了85.4%以及99.6%.相比AVANTI实验中的目标检测方法，减少了在轨任务中相关参数的调节，进一步提升了算法的自主性.     

Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases

Multi-faults detection is a challenge for rolling bearings due to the mode mixture and coupling of multiple fault features, as well as its easy burying in the complex, non-stationary structural vibrations and strong background noises. In this paper, a method based on the flexible analytical wavelet transform (FAWT) possessing fractional scaling and translation factors is proposed to identify multiple faults occurred in different components of rolling bearings. During the route of the proposed method, the proper FAWT bases are constructed via genetic optimization algorithm (GA) based on maximizing the spectral correlated kurtosis (SCK) which is firstly presented and proved to be efficient and effective in indicating interested fault mode. Via using the customized FAWT bases for each interested fault mode, the original vibration measurements are decomposed into fine frequency subbands, and the sensitive subband which enhances the signal-to-noise ratio (SNR) is selected to exhibit the fault signature on its envelope spectrum. The proposed method is tested via simulated signals, and applied to analyze the experimental vibration measurements from the running roller bearings subjected to outrace, inner-race and roller defects. The analysis results validate the effectiveness of the proposed method in identifying multi-faults occurred in different components of rolling bearings.     

基于CMPSO算法的永磁同步电机转动惯量识别研究

永磁同步电机(PMSM)是一种具有很强非线性的动态系统,在工业驱动应用中发挥着重要作用。实时辨识转动惯量对于高精度PMSM系统控制和稳定性状态监测具有重要意义,但传统转动惯量识别方法精度较低。在传统离散模型参考自适应理论基础之上,用柯西变异粒子群优化(CMPSO)算法代替待辨识参数自适律设计环节,以实现PMSM转动惯量识别。该转动惯量辨识方法充分利用了CMPSO算法的快速高效收敛性,仿真结果和试验结果表明了可行性、正确性和准确性。     

基于数字孪生的智能脉动管控

为提升飞机总装脉动生产线智能化水平,开展了基于数字孪生的智能脉动管控研究。基于数字孪生五维模型理论构建了飞机总装脉动生产线数字孪生应用架构,从反应式计划调度、物流精准配送、智能作业指导、产线健康监视、资源迭代优化配置5个方面开展了技术研究,为复杂产品装配的智能制造应用提供参考。     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.     

Inverse identification of constitutive parameters of Ti2AlNb intermetallic alloys based on cooperative particle swarm optimization

Ti₂AlNb intermetallic alloy is a relatively newly developed high-temperature-resistant structural material, which is expected to replace nickel-based super alloys for thermally and mechanically stressed components in aeronautic and automotive engines due to its excellent mechanical properties and high strength retention at elevated temperature. The aim of this work is to present a fast and reliable methodology of inverse identification of constitutive model parameters directly from cutting experiments. FE-machining simulations implemented with a modified Johnson-Cook (TANH) constitutive model are performed to establish the robust link between observables and constitutive parameters. A series of orthogonal cutting experiments with varied cutting parameters is carried out to allow an exact comparison to the 2D FE-simulations. A cooperative particle swarm optimization algorithm is developed and implemented into the Matlab programs to identify the enormous constitutive parameters. Results show that the simulation observables (i.e., cutting forces, chip morphologies, cutting temperature) implemented with the identified optimal material constants have high consistency with those obtained from experiments, which illustrates that the FE-machining models using the identified parameters obtained from the proposed methodology could be predicted in a close agreement to the experiments. Considering the wide range of the applied unknown parameters number, the proposed inverse methodology of identifying constitutive equations shows excellent prospect, and it can be used for other newly developed metal materials.     

A drilling tool design and in situ identification of planetary regolith mechanical parameters

The physical and mechanical properties as well as the heat flux of regolith are critical evidence in the study of planetary origin and evolution. Moreover, the mechanical properties of planetary regolith have great value for guiding future human planetary activities. For planetary subsurface exploration, an inchworm boring robot (IBR) has been proposed to penetrate the regolith, and the mechanical properties of the regolith are expected to be simultaneously investigated during the penetration process using the drilling tool on the IBR. This paper provides a preliminary study of an in situ method for measuring planetary regolith mechanical parameters using a drilling tool on a test bed. A conical-screw drilling tool was designed, and its drilling load characteristics were experimentally analyzed. Based on the drilling tool-regolith interaction model, two identification methods for determining the planetary regolith bearing and shearing parameters are proposed. The bearing and shearing parameters of lunar regolith simulant were successfully determined according to the pressure-sinkage tests and shear tests conducted on the test bed. The effects of the operating parameters on the identification results were also analyzed. The results indicate a feasible scheme for future planetary subsurface exploration.     

基于TLE的弹道系数计算方法及应用分析

针对仅使用两行要素（Two Line Element，TLE）作为数据源的应用需求，研究了基于TLE轨道衰减的弹道系数计算方法。介绍了一种常用的基于两组TLE的直接计算法，分析TLE选取间隔对结果精度的影响；提出了一种基于多组TLE的迭代计算方法，以降低异常TLE对计算结果的影响；从弹道系数计算效果、在再入预报中的应用等方面对这两种方法进行比较分析。结果表明，两种方法各有优劣，基于多组TLE的迭代计算法稳定性更高、受TLE精度的影响更小；由于数据区间更短，基于两组TLE的计算结果对短期轨道衰减特性反应得更准确，用于临近再入时的预报效果更好。     

基于相对最值点的最小二乘直线检测方法

为了解决传统目标检测中遇到的检测速度慢、精度低等问题，本文提出一种新的图像特征——相对最值点，并依据相对最值点提出一种新的最小二乘直线检测的方法。该方法首先搜索Canny边缘的闭合包络，并寻找闭合包络的相对最值点。包络上相邻相对最值点之间长度满足阈值要求的即为疑似直线的两端点，进而利用最小二乘拟合方法获得该疑似直线的拟合方差，最后利用拟合方差与拟合直线长度之比作为直线判定的标准用来检测出图像中的直线。实验表明，本方法与传统方法如PPHT直线检测、LSD直线检测方法等相比，检测直线的精度更高，检测速度更快。     

智能方法在惯性系统误差参数辨识中的应用

总结了目前我国提高惯性系统导航精度的技术途径,阐述了国内外惯性系统误差参数辨识方法的研究现状,介绍了当前滤波算法、智能优化算法和人工神经网络方法在惯性系统导航领域的应用情况以及存在的不足。最后,分析了空间飞行器惯性系统误差参数辨识技术的未来研究方向,即智能优化算法和人工神经网络方法等智能方法将在惯性系统误差参数辨识中发挥越来越重要的作用,通过将误差系数标定问题转换为参数辨识问题,采用智能方法在庞大的解空间内实现对惯性系统误差参数的快速辨识。