基于Boosting-SVM算法的航空发动机故障诊断

提出了一种利用支持向量机(SVM)作为弱基分类器、Boosting算法进行加权融合的航空发动机故障诊断算法.该算法具有支持向量机的强分类能力,又具有Boosting算法适合不均衡数据集的特点.为验证算法的有效性,采用外场实测的滑油光谱分析数据针对传动系统的轴承、减速齿轮和滑油系统3类故障进行了验证.为去除实测数据之间的冗余、降低特征维数,提高算法执行效率,采用主元分析(PCA)和粗糙集理论(RST)进行故障特征压缩和提取.利用实测数据构造了Boosting支持向量机分类器.最后,实验结果表明Boosting-SVM算法可以显著提高SVM分类器的推广性能.针对实测数据,3种故障平均识别准确率由79.4%提高到了85.7%.     

基于支持向量机的组合分类方法及应用

为了解决采用神经网络、决策树作为弱分类器的AdaBoost组合分类存在的不足,进一步改善组合分类效果,提出采用支持向量机(SVM)作为弱分类器的一种新的组合分类诊断方法——AdaBoost-SVM。该方法没有采用一个固定的SVM的核参数,而是自适应调整SVM中的核参数,从而得到一组有效的SVM弱分类器。通过对基准数据库的测试及航空发动机故障样本的诊断,结果表明,所提AdaBoost-SVM方法较好地解决了现有的Ada-Boost组合分类方法中存在的弱分类器本身参数选取困难问题及训练轮数的合理选取问题,并具有更好的泛化性能,更适合对分散程度较大、聚类性较差的航空发动机故障样本进行分类。     

直升机前飞旋翼桨尖涡定常质量射流控制计算

针对直升机前飞状态开展了旋翼非定常桨尖涡的模拟,采用定常桨尖空气质量射流(TAMI)控制方式对旋翼桨尖涡进行了控制。建立了一个适用于前飞旋翼桨尖涡高精度捕捉和质量射流控制模拟的数值方法,在该方法中,采用有限体积法进行空间离散,应用5阶Roe-WENO(weighted essentially non-oscillatory)格式进行流场重构及控制面对流通量计算;采用双时间方法进行时间推进,伪时间步上应用隐式LU-SGS(lower upper symmetric Gauss-Seidal)格式;引入射流边界条件对质量射流进行模拟;采用运动嵌套网格方法对前飞旋翼桨尖的挥舞运动进行模拟,并对桨叶网格和背景网格进行针对性加密。基于所建立的方法对前飞状态旋翼非定常桨尖涡及其质量射流控制进行了模拟,计算结果表明:前飞状态下旋翼桨尖涡存在较大的前后差异,桨盘前侧的桨尖涡涡核强度远小于桨盘后侧;桨盘前侧旋翼桨尖涡的涡核强度很难由定常质量射流控制来减弱,而桨盘后侧的旋翼桨尖涡则可以通过定常质量射流得到有效控制。     

一种基于肌声信号的穿戴式助力系统运动意图检测算法

针对未来深空探测活动中航天员在多种复杂任务环境下的运动助力需求,提出一种面向航天员穿戴式助力系统的运动意图检测算法。以航天员的关节力矩作为运动意图的表征,利用希尔伯特-黄变换对特定肌肉发出的肌声信号进行滤波处理,以消除由肢体运动导致的伪迹噪声和由传感器引入的高频噪声,并参照肌肉的发力原理对滤波后的肌声信号进行特征值提取,通过机器学习的方法建立肌声信号与关节力矩间的映射关系,使助力系统能够及时准确地识别出航天员的运动意图并实施助力。最后募集了3名志愿者进行了150 000组样本数据关节力矩辨识实验,实验结果表明：所提出算法的决定系数可达0.953 2,能够有效辨识航天员的运动意图。     

大载荷摆动情况下飞行器姿态控制研究(英文)

天文观测卫星的主敏感器安装在一个具有二自由度、并直接与卫星平台相连的万向支架上。因敏感器的质量和长度不能忽略,故卫星姿态及其质心位置、转动惯量等结构参数将因敏感器与卫星间的角运动而发生改变,因此大载荷摆动情况下的飞行器姿态控制是本文研究的重点。本文根据动量矩定理推导出了存在内部相对运动的二刚体动力学模型,卫星系统的转动惯量将由该模型确定。由卫星的当前及其期望姿态四元数,构建出描述卫星姿态偏差的拟欧拉角;对拟欧拉角进行规范化处理,以保证三通道所对应的拟欧拉角分量分别由控制力矩的3个分量所控制。之后,提出了基于拟欧拉角的姿态开关控制律,该控制律可保证三通道所对应的相轨迹可沿开关线滑向坐标原点(其期望状态)。仿真结果表明,即使是在结构不对称和三通道严重耦合的情况下,该控制律也能保证卫星姿态可以得到较好的控制。     

头盔伺服系统的主动柔顺控制

 对头盔伺服系统(HMDPM)主动柔顺控制策略的主要内容——轨迹规划和控制方法进行了研究。首先,采用基于力反馈和滑动杆动力学模型的头部运动预测法进行轨迹规划,该方法利用并联机构(PM)分支杆长与运动平台位姿间的映射关系,通过力反馈信息和6-3UPS并联机构滑动杆动力学模型对头部运动进行预测,为头盔伺服系统的位置控制提供期望轨迹;然后,基于头盔伺服系统的动力学模型对系统的惯性项和非线性项进行了计算,设计了惯性项和非线性项补偿控制器,在进行头部运动跟踪的同时,实现了头盔显示器与头部间接触力的控制;最后,采用SimMechanics模块建立了HMDPM—人交互模型,并进行了相关验证实验。仿真结果表明,基于力反馈和滑动副滑动杆动力学模型的头部运动预测法能实时地、较为准确地预测出头部运动位置;基于动力学模型的惯性和非线性项补偿控制器不仅可以较为准确地跟踪头部运动,而且还能有效地减小头盔显示器与头部间的接触力,降低执行机构的刚度、减少系统摩擦力等非线性因素对使用者的干扰。     

环火探测器非球形引力摄动运动的频率分析方法

环火探测器的摄动分析解对于分析探测器轨道运动规律以及星载计算具有比较重要的意义。针对火星非球形引力摄动影响下环火探测器的运动,采用频率分析方法,对环火探测器轨道运动进行仿真,得到了与KAM理论一致的结果,在火星非球形引力摄动作用下的环火探测器轨道位于一个不变环面,可由3个角变量的频率描述。利用该方法,采用数值方法构建轨道运动的分析表达式,通过与数值积分结果的比对,证明该分析表达式具有较好的精度,适合长时间的轨道外推,可以满足航天任务的星载应用需求。该方法不仅可以给出环火探测器在任意时刻的轨道状态量,同时可以较高精度地确定3个角变量的变化率,反映一定的轨道变化规律。     

带等离子控制的飞翼布局飞机模型的风洞虚拟飞行试验

等离子体流动控制技术已经在流动控制领域成为热点和焦点。为了研究等离子体对于飞翼布局飞机稳定性的影响,本文研究中采用闭环飞行控制律对飞翼布局飞机模型的操纵舵面进行操控,同时增加等离子控制,对该模型飞机在失速迎角附近区域开展三自由度(3DoF)的虚拟飞行试验研究。结果表明,等离子打开后,在俯仰运动上,使得飞机俯仰振荡幅值变小,增快振荡衰减,在滚转运动上,对滚转角命令的跟随性变好;在偏航运动上,增加了偏航阻尼,改善了原来偏航运动的偏离问题。因此,等离子流动控制对于飞翼布局飞机在失速迎角附近的稳定性改善具有良好的效果,对未来等离子技术的实际应用提供了借鉴和指导。     

基于FANUC 0i系统的数控铣床的坐标旋转指令的使用研究

对教材上的坐标旋转指令(G68)应用例题的程序进行仿真加工,结果发现与图纸要求的形状不相符,分析其原因是该指令没有坐标原点平移的功能。要正确使用该指令实现加工,可以先采用坐标原点平移,再旋转的方法;或采用先控制刀具运动到旋转中心,再执行旋转指令,旋转指令后的程序段都采用增量值编程的方法。对这两种方法进行编程和仿真加工,结果显示符合图纸要求。     

基于SVM的下肢运动表面肌电信号的特征提取与辨识分析

为了对蹲、站、行走支撑与行走摆动四类人体典型运动行为进行有效的分类辨识,三位健康且未经过专业训练的受试者被邀请参加运动实验,实验对人体下肢股内侧肌的表面肌电信号(Electromyography,EMG)进行实时采集和记录。通过时域、频域、时频域方法,对特征值进行提取,发现下肢股内侧肌的表面肌电信号在下蹲、起立、行走支撑期和行走摆动期四类运动方式下的动态特征具有明显的差异性。基于上述结论,介绍了一种基于支持向量机(Support Vector Machine,SVM)的误识别样本二次分类方法,对上述典型运动类型进行了辨识分析。与传统单次样本识别结果相比,本文所发展的基于SVM特征值分析的误识别样本二次分类方法能较好地提高识别效果,样本辨识结果显示时域和时频域的识别效果最好,时频域方法抗外界干扰能力较好。     

Hybrid discriminative/class-specific classifiers for narrowband signals

The class-specific (CS) method of signal classification operates by computing low-dimensional feature sets defined for each signal class of interest. By computing separate feature sets tailored to each class, i.e., CS features, the CS method avoids estimating probability distributions in a high-dimension feature space common to all classes. Building a CS classifier amounts to designing feature extraction modules for each class of interest. In this paper we present the design of three CS modules used to form a CS classifier for narrowband signals of finite duration. A general module for narrowband signals based on a narrowband tracker is described. The only assumptions this module makes regarding the time evolution of the signal spectrum are: (1) one or more narrowband lines are present, and (2) the lines wandered either not at all, e.g., CW signal, or with a purpose, e.g., swept FM signal. The other two modules are suited for specific classes of waveforms and assume some a priori knowledge of the signal is available from training data. For in situ training, the tracker-based module can be used to detect as yet unobserved waveforms and classify them into general categories, for example short CW, long CW, fast FM, slow FM, etc. Waveform-specific class-models can then be designed using these waveforms for training. Classification results are presented comparing the performance of a probabilistic conventional classifier with that of a CS classifier built from general modules and a CS classifier built from waveform-specific modules. Results are also presented for hybrid discriminative/generative versions of the classifiers to illustrate the performance gains attainable in using a hybrid over a generative classifier alone.     

An effective crack position diagnosis method for the hollow shaft rotor system based on the convolutional neural network and deep metric learning

In recent years, the crack fault is one of the most common faults in the rotor system and it is still a challenge for crack position diagnosis in the hollow shaft rotor system. In this paper, a method based on the Convolutional Neural Network and deep metric learning (CNN-C) is proposed to effectively identify the crack position for a hollow shaft rotor system. Center-loss function is used to enhance the performance of neural network. Main contributions include: Firstly, the dynamic response of the dual-disks hollow shaft rotor system is obtained. The analysis results show that the crack will cause super-harmonic resonance, and the peak value of it is closely related to the position and depth of the crack. In addition, the amplitude near the non-resonant region also has relationship with the crack parameters. Secondly, we proposed an effective crack position diagnosis method which has the highest 99.04% recognition accuracy compared with other algorithms. Then, the influence of penalty factor on CNN-C performance is analyzed, which shows that too high penalty factor will lead to the decline of the neural network performance. Finally, the feature vectors are visualized via t-distributed Stochastic Neighbor Embedding (t-SNE). Naive Bayes classifier (NB) and K-Nearest Neighbor algorithm (KNN) are used to verify the validity of the feature vectors extracted by CNN-C. The results show that NB and KNN have more regular decision boundaries and higher recognition accuracy on the feature vectors data set extracted by CNN-C, indicating that the feature vectors extracted by CNN-C have great intra-class compactness and inter-class separability.     

Adaptive learning approach to landmine detection

We consider landmine detection using forward-looking ground penetrating radar (FLGPR). The two main challenging tasks include extracting intricate structures of target signals and adapting a classifier to the surrounding environment through learning. Through the time-frequency (TF) analysis, we find that the most discriminant information is TF localized. This observation motivates us to use the over-complete wavelet packet transform (WPT) to sparsely represent signals with the discriminant information encoded into several bases. Then the sequential floating forward selection method is used to extract these components and thereby a neural network (NNW) classifier is designed. To further improve the classification performance and deal with the problem of detecting mines in an unconstraint environment, the AdaBoost algorithm is used. We integrate the feature selection process into the original AdaBoost algorithm. In each iteration, AdaBoost identifies the hard-to-learn examples and a new set of features which provide the specific discriminant information for these hard samples is extracted adaptively and a new classifier is trained. Experimental results based on measured data are presented, showing that a significant improvement on the classification performance can be achieved.     

基于SVM和广义粗糙度特征的航空发动机振动故障诊断方法

通过对航空发动机振动信号进行小波分解,依据多尺度空间局部能量分布和粗糙性提取基于子带信号能量加权广义粗糙度特征实现对振动情况的描述.然后将上述特征送入支持向量机(support vector machine,简称SVM)分类器进行训练,根据分类器的输出结果判断航空发动机的工作状态和故障类型.通过对实测航空发动机试车时得到的振动信号的实验分析结果表明,该算法可以有效地识别发动机的振动故障.      

Parametric models for helicopter identification using ANN

An artificial neural network (ANN) based helicopter identification system is proposed. The feature vectors are based on both the tonal and the broadband spectrum of the helicopter signal, ANN pattern classifiers are trained using various parametric spectral representation techniques. Specifically, linear prediction, reflection coefficients, cepstrum, and line spectral frequencies (LSF) are compared in terms of recognition accuracy and robustness against additive noise. Finally, an 8-helicopter ANN classifier is evaluated. It is also shown that the classifier performance is dramatically improved if it is trained using both clean data and data corrupted with additive noise.     

Source free unsupervised domain adaptation for electro-mechanical actuator fault diagnosis

A common necessity for prior unsupervised domain adaptation methods that can improve the domain adaptation in unlabeled target domain dataset is access to source domain dataset and target domain dataset simultaneously. However, data privacy makes it not always possible to access source domain dataset and target domain dataset in actual industrial equipment simultaneously, especially for aviation component like Electro-Mechanical Actuator (EMA) whose dataset are often not shareable due to the data copyright and confidentiality. To address this problem, this paper proposes a source free unsupervised domain adaptation framework for EMA fault diagnosis. The proposed framework is a combination of feature network and classifier. Firstly, source domain datasets are only applied to train a source model. Secondly, the well-trained source model is transferred to target domain and classifier is frozen based on source domain hypothesis. Thirdly, nearest centroid filtering is introduced to filter the reliable pseudo labels for unlabeled target domain dataset, and finally, supervised learning and pseudo label clustering are applied to fine-tune the transferred model. In comparison with several traditional unsupervised domain adaptation methods, case studies based on low- and high-frequency monitoring signals on EMA indicate the effectiveness of the proposed method.     

FRFT域能量谱模拟电路故障特征提取方法

故障特征提取是模拟电路故障诊断的关键技术之一,为了提高故障特征的可诊性,提出 1种基于分数阶傅里摘叶变换(Fractional Fourier Transform,FRFT)域能量谱的模拟电路故障特征提取方法。首先,采集测试节点电压信号并将其映射到不同的 FRFT域空间中(p从 0变化到 1);然后,计算所有 FRFT域空间中的能量谱峰值并将其作为故障特征;最后,将归一化后的特征用于训练最近邻分类器进行诊断验证。与现有的 FRFT故障特征提取方法相比,该方法减少了计算量,且提取的特征能够在所有 FRFT域中更全面地反映不同故障响应信号的细微差异,有利于提高故障特征的可分性。在仿真和物理电路上进行了验证,实验结果表明:所提方法能提高故障诊断准确率,且时间复杂度有明显改善。     

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.     

Adaptive boosting for SAR automatic target recognition

The paper proposed a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, the multiclass problem was decomposed into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature     

基于LMD与AO-PNN的中介轴承故障诊断方法

针对航空发动机中介轴承受噪声干扰大、传递路径复杂导致采用传统方法难以进行故障诊断的问题,提出了一种基于局部均值分解（LMD）与相关系数-能量比-峭度准则、结合天鹰座优化算法（AO）优化概率神经网络（PNN）的中介轴承故障诊断方法。使用LMD对传感器采集的振动信号进行分解;利用相关系数-能量比-峭度准则判决筛选分解得到的PF分量,重构筛选后的信号;计算重构信号的多尺度排列熵（MPE）,以构建特征向量;通过AO优化的PNN的平滑因子,将优化后的神经网络用于中介轴承的故障诊断。基于中介轴承故障试验数据对诊断结果进行了分析,结果表明：提出的方法可以有效诊断高背景噪声、复杂路径干扰下的航空发动机中介轴承的典型故障,与粒子群优化的概率神经网络方法（PSO-PNN）和传统的PNN方法相比,其诊断准确率分别提高了3.875%和8.125%,具有较好的全局收敛性和计算鲁棒性。