Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.     

无监督深度学习单目视觉里程计研究

针对传统单目视觉里程计存在的尺度漂移和尺度不一致问题，提出了一种基于无监督深度学习的单目视觉里程计。首先，联合使用空间几何约束和图像相似性约束，得到长序列尺度一致的深度估计网络和鲁棒的光流估计网络；然后，对密集光流进行采样，得到精确的稀疏对应关系，减少尺度漂移；最后，根据改进的ORB-SLAM初始化方法，选择最优跟踪方式，结合深度信息进行尺度对齐，从而得到全局尺度一致的视觉里程计。在KITTI数据集上进行大量实验，结果表明，相较于ORB-SLAM2和基于深度学习的端到端的视觉里程计系统，该算法在通用评估指标方面性能有明显提升，验证了该算法的有效性。     

Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

Impulse components in vibration signals are important fault features of complex machines. Sparse coding(SC) algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding(FSC) and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.     

Prior-guided GAN-based interactive airplane engine damage image augmentation method

Deep learning-based methods have achieved remarkable success in object detection, but this success requires the availability of a large number of training images. Collecting sufficient training images is difficult in detecting damages of airplane engines. Directly augmenting images by rotation, flipping, and random cropping cannot further improve the generalization ability of existing deep models. We propose an interactive augmentation method for airplane engine damage images using a prior-guided GAN to augment training images. Our method can generate many types of damages on arbitrary image regions according to the strokes of users. The proposed model consists of a prior network and a GAN. The Prior network generates a shape prior vector, which is used to encode the information of user strokes. The GAN takes the shape prior vector and random noise vectors to generate candidate damages. Final damages are pasted on the given positions of background images with an improved Poisson fusion. We compare the proposed method with traditional data augmentation methods by training airplane engine damage detectors with state-of-the-art object detectors, namely, Mask R-CNN, SSD, and YOLO v5. Experimental results show that training with images generated by our proposed data augmentation method achieves a better detection performance than that by traditional data augmentation methods.     

Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.     

Softmax分类器深度学习图像分类方法应用综述

基于深度学习的人工智能图像分类方法研究是当前计算机视觉领域的研究热点。面向深度学习中的Softmax图像分类方法，首先回顾了图像分类技术的发展历程，接着介绍了图像识别技术中的分类器，并解释了Softmax回归函数的分类实现原理。基于Softmax回归分类器的应用，详细阐述了多种图像分类技术，具体包括浅层神经网络、深度置信网络、深度自编码器和卷积神经网络。同时，对比介绍了各种级联模型的具体结构、训练方法、实际应用、分类效果以及优缺点。最后，从Softmax回归分类器、深度学习网络模型和高维数据分类三个方面对基于Softmax回归分类器的深度学习模型在图像分类方面的发展与应用前景进行了展望。     

基于深度学习的地空导弹发射区拟合算法

目前地空导弹发射区的拟合算法主要是多项式拟合法和BP神经网络拟合法。多项式拟合法存在函数形式难以确定、函数范围不易分段等问题，且拟合精度较低；传统神经网络方法要想达到较高精度，需要大量的隐层节点，且在隐层节点数增加到一定程度后，训练变得十分困难且精度很难继续提高。同时，传统神经网络需要大量的标签数据，进一步增大了实际应用的难度。为此，基于深度学习理论，设计了一种基于堆栈稀疏自编码器（SSAE）的深度拟合网络（DFN），并给出了相应的训练策略。仿真实验表明其相比传统算法具有更小的拟合误差优势。所设计的深度稀疏自编码器网络可以克服多项式拟合和传统神经网络的不足，不仅可以在大量无标签数据和少量标签数据条件下进行学习训练，而且可以进一步提升地空导弹发射区的拟合精度。     

基于RDK-ELM 的航空发动机控制系统故障诊断

为保持较高诊断正确率,缩短训练时间,满足航空发动机故障诊断对于实时性和高诊断率的需求,提出1种对深度核极限学习机的简约改进方法。输入数据中随机选取部分数据作为支持向量,结合深度学习网络的多层结构,完成了对输入样本的特征提取,通过核函数实现了高维空间映射分类。数字仿真表明:算法分类正确率高,训练时间短,可应用于航空发动机控制系统的故障诊断。     

基于深度长短期记忆网络的发动机叶片剩余寿命预测

为了研究航空发动机转子叶片的剩余寿命预测问题,提出了一种基于多传感器信号融合的深度长短期记忆网络(DLSTM)预测模型。首先利用深度学习和长短期记忆的组合来构造DLSTM网络。然后,将多个传感器信号数据进行融合处理,从而通过深度学习发现各个传感器时序信号之间隐藏的长期依赖关系。进一步在给定网格搜索策略的情况下,通过自适应矩估计算法调整DLSTM的网络结构和参数,并且在DLSTM模型中引入了一种随机丢失策略,以缓解过度拟合问题并使预测模型规范化。最后利用CMAPSS涡扇发动机进行了实验验证,在一种故障模式和两种故障模式条件下,DLSTM网络预测模型相对于其他传统方法的评价指标Score分别下降了17.19%和14.37%,其他两个评价指标相对来说也较优,结果表明本文提出的方法具有更高的准确性以及稳定性。     

多重信号分类算法测向精度的仿真分析

作为空间谱估计理论体系中的标志性算法,多重信号分类算法从1979年提出后就一直是阵列信号处理领域的研究热点之一。针对复杂电磁环境中阵列测向的实际应用问题,从多信源、含噪宽带信号、入射角度、阵列间距这4个因素对多重信号分类算法的测向精度作了仿真分析,得出了一些结论。     

面向智能感知的小样本学习研究综述

小样本学习指只利用目标类别的少量监督信息来训练机器学习模型。由于其实用价值，学术界和工业界提出很多针对该问题的解决方案，但是目前国内缺少该问题的综述。本文对国内外学者提出的小样本学习算法及基于小样本学习的目标检测算法进行了系统的总结和探索。首先，给出了小样本学习的问题定义，列举其与其他一些经典的机器学习问题之间的联系，同时从理论上阐述小样本学习问题面临的挑战；接着，对基于小样本学习的图像分类进行了概述，并对其中代表性的工作进行介绍与分析；在此基础上，重点针对基于小样本学习的目标检测，特别是零样本条件下的目标检测问题，详细介绍和分析了现有的研究工作；最后，立足于现有方法的优缺点，从问题设定、理论研究、实现技术以及应用场景等几个方面对小样本学习的未来发展进行了展望，期望为该领域后续的研究工作提供启示。     

基于卷积门控循环网络的滚动轴承故障诊断

针对许多基于深度学习的滚动轴承故障诊断方法在小样本数据集下诊断性能下降的问题,提出一种基于卷积门控循环神经网络的轴承故障诊断模型。该模型使用两层的卷积网络来从输入信号中提取特征,同时使用tanh函数作为激活函数,且池化层使用大池化核来进行重叠下采样。将所提取得到的高层特征连接到双向门控循环网络。合并循环网络正向和逆向的最后一个状态,并连接一层全连接层进行输出。选用凯斯西储大学的轴承故障数据集来验证模型在小样本数据集下的诊断性能,实验结果表明,相比于其他类型的模型,该模型在仅有20个训练样本的情况下依然保持97%的识别准确率。      

卷积神经网络和峭度在轴承故障诊断中的应用

针对传统智能诊断方法依靠专家知识和人工提取数据特征工作量大的问题,结合深度学习方法在特征提取和处理大数据方面的优势,研究了一种基于卷积神经网络和振动信号峭度指标的滚动轴承故障诊断方法。该方法将深度学习应用于轴承故障诊断,提取滚动轴承正常状态、内圈故障、外圈故障和滚动体故障4种状态的振动信号,将振动信号分段处理得到峭度指标,使用数据到图像的转换方法将峭度指标转换为灰度图,送入卷积神经网络模型完成故障分类。在进行滚动轴承故障诊断的实验时,所提的模型诊断准确率达到99.5%,高于传统支持向量机(SVM)算法的95.8%。      

Drogue detection for autonomous aerial refueling based on convolutional neural networks

Drogue detection is a fundamental issue during the close docking phase of autonomous aerial refueling(AAR). To cope with this issue, a novel and effective method based on deep learning with convolutional neural networks(CNNs) is proposed. In order to ensure its robustness and wide application, a deep learning dataset of images was prepared by utilizing real data of ‘‘Probe and Drogue" aerial refueling, which contains diverse drogues in various environmental conditions without artificial features placed on the drogues. By employing deep learning ideas and graphics processing units(GPUs), a model for drogue detection using a Caffe deep learning framework with CNNs was designed to ensure the method's accuracy and real-time performance. Experiments were conducted to demonstrate the effectiveness of the proposed method, and results based on real AAR data compare its performance to other methods, validating the accuracy, speed, and robustness of its drogue detection ability.     

An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making. Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, in real-world applications, manual sample labeling is often time-consuming and laborious. In addition, airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time. To solve these problems, in this paper, an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning, ensemble learning, semi-supervised learning, and Tri-training algorithm, abbreviated as Online Ensemble Semi-supervised Classification Framework (OESCF), is proposed. The framework is divided into four parts: basic classifier offline training stage, online recognition model initialization stage, target maneuver online recognition stage, and online model update stage. Firstly, based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement, basic classifiers are trained offline by making full use of labeled and unlabeled sample data. Secondly, the dynamic density clustering algorithm of the target maneuver is performed, statistical information of each cluster is calculated, and a set of micro-clusters is obtained to initialize the online recognition model. Thirdly, the ensemble K-Nearest Neighbor (KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances. Finally, to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat, the parameters of the model are updated online using error-driven representation learning, exponential decay function and basic classifier obtained in the offline training stage. The experimental results on several University of California Irvine (UCI) datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models, and the results show that the proposed model achieves higher classification accuracy.     

Unintentional modulation evaluation in time domain and frequency domain

With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an Un Intentional Modulation(UIM), namely Radio Frequency Fingerprint(RFF). RFFs can be used to uniquely identify an emitter ...     

基于机器视觉技术的飞机绕机外观检查方法研究

大型民用飞机试飞和航线运营期间,对其外观表面进行绕机外观检查是适航性安全检查的必要工作。目前飞机的绕机检查主要采用人工绕机方式,该方式,且成本高、效率低,易出现漏检、误检等人为因素,因此智能外观表面检查方法的研究是一项迫切的任务。相比于其他工业检测任务,飞机外观检查智能识别目前无公开数据集,且飞机真实外观损伤类型多样。通过对飞机外观图像的采集和处理,研究基于YOLO V3的飞机表面检查工程方法,初步建立了飞机外观损伤图像数据集框架。首先采用YOLO检测网络粗略获取飞机外观损伤位置和损伤类型,其次针对不同损伤类型的特点,用水平集算法获取图像块中更精准的损伤位置,最后根据精细化后的结果进行量化分析。提出了能够解决机器深度学习网络智能检测已知类别损伤的方法,对未知损伤具有较强容忍度和较大的灵活性与适应性。实践表明,本文提出的方法可以解决传统人工目视检测的部分弊端,可以为机器人智能绕机检查的工程应用提供技术参考,对降低飞机试飞和运营阶段的维修、维护成本有重要意义。     

Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

Bearing pitting, one of the common faults in mechanical systems, is a research hotspot in both academia and industry. Traditional fault diagnosis methods for bearings are based on manual experience with low diagnostic efficiency. This study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout regularization. Deep separable convolution extracts features from the raw bearing vibration signals, during which a 3 × 1 convolutional kernel with a one-step size selects effective features by adjusting its weights. The similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller weights. The spatial dropout regularization method focuses on bearing signal fault features, improving the independence between the bearing signal features and enhancing the robustness of the model. A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability improvement. To validate the effectiveness of the proposed method, we collect raw vibration signals from bearings in eight different health states. The experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.     

基于卷积双向长短期记忆网络的轴承故障尺寸估计

基于振动监测数据的航空发动机滚动轴承损伤大小识别,对于研究滚动轴承故障演化、故障预测和故障诊断具有重要意义。针对传统模型对先验知识依赖性高、特征提取不充分、故障尺寸训练类别有限等问题,提出了一种基于深度学习的滚动轴承损伤尺寸预计方法,能够对训练过程中未出现的中间尺寸进行准确识别。在经典模型的基础上,搭建了一种深度卷积网络与长短期记忆网络组合模型,该模型可对轴承振动信号的多维特征与时序特征进行充分提取,实现轴承故障的智能和高效诊断。最后,利用滚动轴承加速疲劳试验机,进行了多种转速与损伤尺寸下的滚动轴承故障试验,基于试验数据进行了方法的比较,结果表明,该组合网络的在正常和加噪的情况下预测精度分别达到99.94%和98.67%,较单独的深度卷积网络、长短期记忆网络及其他模型精度更高,比较结果充分表明了本文所提方法的优越性。     

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.