基于改进深度卷积神经网络的轴承故障诊断

轴承为风电机组的重要且故障频发部件,传统基于轴承振动数据的图像转换的卷积神经网络(CNN)的故障诊断技术存在一定局限性。提出了一种基于改进深度卷积神经网络(IDCNN)的直接时间序列特征提取方法,依据采样频率将原始振动数据划分为单个样本,构建诊断模型训练数据集。设计了一种新型的深度卷积神经网络(IDCNN),自动提取复杂样本数据的故障特征,提高DCNN的鲁棒性和泛化性,并将IDCNN提取的高维故障特征输入到分类器中,从而实现轴承故障的智能诊断。对比实验结果表明本方法有效提升了故障诊断精度。     

基于信号特征提取和卷积神经网络的轴承故障诊断研究

轴承是机械设备主要零部件之一,也是机械设备主要故障零部件之一。轴承故障问题为机械设备的重点,机械设备的使用受到故障轴承的直接影响。针对传统的卷积神经网络算法轴承故障诊断效率低下问题,本文提出了一种基于信号特征提取和卷积神经网络的优化方法。首先对原始数据信号进行时域和频域的信号特征提取,获得有效的故障特征值。之后,使用卷积神经网络对提取的特征值进行故障诊断,完成故障分类。本文使用美国凯斯西储大学的滚动轴承振动加速度信号作为数据集,对提出的方法进行验证,得到的故障诊断平均准确率为74.37%,准确率的方差为0.0001;传统的卷积神经网络算法故障诊断平均准确率为65.6%;准确率的方差为0.0019。实验结果表明,相比传统的卷积神经网络,提出的方法对轴承故障诊断的准确率有显著的提高,并且该方法的稳定性更佳,计算时间更少,综合性能更佳。     

基于全双谱和卷积神经网络的信号分类方法

为解决电磁频谱中的未知信号分类和身份识别问题,提出一种基于改进卷积神经网络（CNN）LeNet-5模型的信号分类方法。该方法使用信号全双谱做为CNN的输入,然后通过改进的LeNet-5模型学习信号特征并完成信号分类和身份识别。实验结果表明,算法对未知信号调制类型识别率达97%以上,对信号身份属性识别率达96%以上。相比传统方法,该算法对信号身份属性识别率提高6.5%,具有更好的泛化性能,并有效解决了全双谱应用的二维模板匹配和Loss函数值下降缓慢问题。     

改进胶囊网络优化分层卷积的亚健康识别算法

针对传统卷积神经网络(CNN)为获得高准确率不断堆叠卷积层、池化层致使模型结构复杂、训练时间长且数据处理方式单一的问题,提出改进胶囊网络优化分层卷积的亚健康识别算法.首先,对原始振动数据进行小波降噪和小波包降噪两种数据处理,更好地保留原始信号中对亚健康识别有用的信息;其次,CNN采用分层卷积的思想,并行3个不同尺度的卷...     

基于Teager能量谱和卷积神经网络的滚动轴承故障诊断

在智能制造环境下,针对滚动轴承在变负载驱动环境下特征提取难、故障诊断准确率低的问题,提出基于Teager能量谱和卷积神经网络的滚动轴承故障诊断方法.将不同负载驱动下的多种故障振动信号,通过计算Teager能量算子之后进行快速傅里叶变换,绘图得到Teager能量谱图,形成数据集.使用数据集训练改进的卷积神经网络,得到滚动轴承的故障诊断模型,并通过该模型进行故障诊断.经过实验验证,在变负载驱动环境下,使用Teager能量谱图进行故障诊断结果优于使用原始信号时域图和频域图,轴承不同故障的诊断准确率达到93.35％,同时方法使用卷积神经网络解决了人工提取特征不全面、诊断效率低的问题,具有一定的实用性.     

基于卷积图神经网络的滚动轴承故障诊断算法

针对常规深度学习方法在直接处理一维时域振动信号进行故障诊断时诊断准确度较低的问题,提出了一种基于一维卷积神经网络(Convolution Neural Network,CNN)与图神经网络(Graph Neural Network,GNN)的滚动轴承故障诊断算法(CGNN).首先通过一维卷积层对振动信号做自适应滤波与数...     

基于一维卷积神经网络的自动扶梯机械故障分类研究

老旧扶梯机械故障较为隐蔽,定期检验不易发现,且对扶梯机械故障的智能分类的研究较少。自动扶梯振动信号复杂多变,数据量大,而采用传统机器学习算法对其机械故障进行诊断效果不佳。为实现自动扶梯机械故障的智能分类,在经典二维卷积神经网络的基础上,引入了卷积核的一维卷积神经网络,构建了自动扶梯机械故障的自动分类模型。首先为提高模型的泛化性能,融合凯斯西储大学轴承故障、东南大学齿轮故障和某大型商场自动扶梯梯级滚轮磨损故障的复合故障数据建立了数据集。然后用数据增强的方法对数据进行预处理,接着采用一维卷积神经网络,构建自动扶梯机械故障诊断模型。最后使用测试数据集对模型的分类精度进行了验证实验,结果表明该模型有着比传统机器学习算法自动化程度高、成本低、专业门槛低、步骤简单等明显优势,而且该模型能快速准确地对自动扶梯的机械故障进行自动诊断,实现了95%的诊断准确率,为下一步将该算法集成到检验仪器中打下了基础。     

基于VGGNet的人脸表情识别研究

针对传统卷积神经网络(Convolutional Neural Network,CNN)在人脸表情识别中有效特征提取不够、泛化能力不强及识别准确性不高等缺点,研究选用具有更小卷积核和更深池化层的视觉几何组网络(Visual Geometry Group Network,VGGNet)进行人脸表情识别系统的设计。为了验证识别效果,在传统CNN和VGGNet框架下进行人脸表情识别系统的搭建,使用FER2013公开数据集进行训练和测试,并对测试结果进行对比分析。实验表明,传统CNN模型在人脸表情识别任务中的识别率仅为88%,而VGGNet则能够取得高达98%的识别率。此外,利用真人实际测试验证了研究搭建的VGGNet模型具有很好的表情识别效果。     

一种面向噪声环境中旋转机械故障诊断的多模态耦合输入神经网络

旋转机械设备是工业生产中的关键性设备,对其进行高效故障诊断,对于保障工业安全生产具有重要意义.传统的旋转机械设备智能故障诊断方法采取人工特征提取策略,存在依赖专家经验知识、特征泛化性差、特征完备性不足等局限性,导致故障诊断模型精度差,特别是在噪声环境下性能下降明显.对此,提出一种用于旋转机械故障诊断的多模态耦合输入神经网络模型.首先,利用信号分解方法将原始输入信号分解为多个子信号,并将子信号与原始信号成对组成二维矩阵并输入到神经网络中,使得网络能够提取其间重要的相关特征;然后,利用双通道并行的卷积神经网络和长短期记忆网络分别提取信号中的时空间特征并融合,大大提高网络模型的特征表达完备性,实现对旋转机械设备的高精度故障分类.通过实验验证了所提出模型相较于传统故障模型具有更高的准确率,并且对于噪声干扰也有较好的适应性.     

基于空洞卷积神经网络的旋转机械故障诊断方法

由于旋转机械的振动信号具有非平稳、复杂多样、数据量大的特点,传统的方法难以较好地实现旋转机械故障诊断。近年来,基于深度学习的故障诊断算法发展迅速,其中,卷积神经网络(Convolutional Neural Network,CNN)由于可实现自动提取特征、运算效率高等优点受到广泛关注,但在识别准确率等方面仍然存在部分问题。为实现多传感器监测状态下的旋转机械故障诊断,在经典卷积神经网络结构的基础上,引入了多通道数据融合处理、空洞卷积层、批标准化处理、PReLU激活函数、全局平均池化层等改进方法,构造了一种新型的、高效的空洞卷积神经网络(Atrous Convolution-Convolutional Neural Network,AC-CNN),并基于该模型进行了旋转机械故障诊断实验。实验结果表明,提出的故障诊断模型分类准确率可达99%以上,对比其他神经网络方法具有明显优势。     

One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes

Noise and high-dimension of process signals decrease effectiveness of those regular fault detection and diagnosis models in multivariate processes. Deep learning technique shows very excellent performance in high-level feature learning from image and visual data. However, the large labeled data are required for deep neural networks (DNNs) with supervised learning like convolutional neural network (CNN), which increases the time cost of model construction significantly. A new DNN model, one-dimensional convolutional auto-encoder (1D-CAE) is proposed for fault detection and diagnosis of multivariate processes in this paper. 1D-CAE is utilized to learn hierarchical feature representations through noise reduction of high-dimensional process signals. Auto-encoder integrated with convolutional kernels and pooling units allows feature extraction to be particularly effective, which is of great importance for fault detection and diagnosis in multivariate processes. The comparison between 1D-CAE and other typical DNNs illustrates effectiveness of 1D-CAE for fault detection and diagnosis on Tennessee Eastman Process and Fed-batch fermentation penicillin process. The proposed method provides an effective platform for deep-learning-based process fault detection and diagnosis of multivariate processes.     

基于深度度量学习的电机故障诊断

深度学习以其强大的自适应特征提取和分类能力在机械大数据处理方面取得了丰硕的成果,由于电机结构的复杂性,其信号表现出的非平稳、非线性和复杂多样等特点,使得传统分类方法中的Softmax分类器+交叉熵损失函数对电机故障诊断力不从心。根据电机信号非平稳、数据量大等特点,结合短时傅里叶变换(STFT)与深度学习中的卷积神经网络(CNN)算法和Triplet Loss三元组思想,提出了深度度量学习电机故障诊断方法。该方法能将电机故障信号转换成时频谱图,同时构建CNN,将预处理后的样本用于CNN的训练,采用Triplet Loss作为损失函数度量故障数据高维特征间的距离,并结合标签有监督地微调整个网络,从而实现准确的电机故障诊断。实验表明该方法在处理复杂数据时能够度量特征在高维空间中的距离,高效完成故障诊断任务,弥补了交叉熵函数的不足。     

基于改进CNN的串联型故障电弧识别方法研究

针对串联型故障电弧影响供电系统安全且不易测量的问题,提出改进卷积神经网络对串联型故障电弧的识别方法.由于SVM学习的超平面是距离各个样本最远的平面,相比于Softmax,具有更强的泛化推广能力和更高的识别准确率,故采用SVM损失函数(hinge loss)替换原有的Softmax损失函数并在CNN模型中添加三层Ince...     

Series arc fault diagnosis method of photovoltaic arrays based on GASF and improved DCGAN

In recent years, the methods of machine learning are widely investigated to resolve the series arc fault (SAF) diagnosis problem in photovoltaic (PV) arrays. However, owing to the factors such as weak signal characteristics, long algorithm execution time, and sample imbalance in practical applications, these methods may have difficulties of detecting the SAF. To address these problems, a method based on the Gramian angular summation field (GASF) combined with the squeeze and excitation-deep convolution generative adversarial network (SE-DCGAN) is proposed. Firstly, the absolute difference of margin factor (ADMF) of the current signal is calculated to accurately extract the transient current data when the SAF occurs. Thereafter, the GASF is used to convert transient current data into two-dimensional images to amplify the universal characteristics of the SAF. Subsequently, the SE-DCGAN is adopted to augment the GASF images of the SAF to solve the problem of limited SAF samples. Finally, a convolutional neural network (CNN) is trained to identify the SAF. Also, a fusion sample training method is proposed in this research, that is, normal samples of different PV systems are added to the training set to enhance the generalization ability of CNN. The advantages of the proposed method are that the identification of SAF is improved by converting one-dimensional signals into two-dimensional images, and the generalization ability of the detection model is improved by exploiting the common features of SAFs and fusion training. The validity and generalization ability of the proposed method are verified by three datasets under different PV systems. Experimental results reveal that the proposed method can achieve high recognition accuracy for the measured data; moreover, no misjudgments occurred in identifying the interference events such as maximum power point tracking (MPPT) adjustment and irradiance mutation (IM). In addition, the experiments confirm that the fusion training method enables the model more universal and applicable.     

A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems

Fault diagnosis plays an important role in actual production activities. As large amounts of data can be collected efficiently and economically, data-driven methods based on deep learning have achieved remarkable results of fault diagnosis of complex systems due to their superiority in feature extraction. However, existing techniques rarely consider time delay of occurrence of faults, which affects the performance of fault diagnosis. In this paper, by synthetically considering feature extraction and time delay of occurrence of faults, we propose a novel fault diagnosis method that consists of two parts, namely, sliding window processing and CNN-LSTM model based on a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). Firstly, samples obtained from multivariate time series by the sliding window processing integrates feature information and time delay information. Then, the obtained samples are fed into the proposed CNN-LSTM model including CNN layers and LSTM layers. The CNN layers perform feature learning without relying on prior knowledge. Time delay information is captured with the use of the LSTM layers. The fault diagnosis of the Tennessee Eastman chemical process is addressed, and it is verified that the predictive accuracy and noise sensitivity of fault diagnosis can be greatly improved when the proposed method is applied. Comparisons with five existing fault diagnosis methods show the superiority of the proposed method.

     

基于信息融合与CNN的齿轮箱故障诊断方法

齿轮箱在实际生产中面临复杂多变的工况,其部件的故障特征随工况发生改变,常规方法在变工况下难以有效识别故障。针对该问题,提出一种基于信息融合和卷积神经网络(IFCNN)的故障诊断方法。IFCNN使用多传感器信息融合和多域特征融合改进卷积神经网络(CNN),首先将不同位置的加速度传感器采集到的振动信号转换成频域、时频域信息,将来自不同传感器的信息融合,然后用CNN对故障信号的频域、时频域信息分别进行特征提取和多域特征融合,结合注意力机制选择重要特征进行故障分类。多组实验结果表明,IFCNN在变工况场景下,可有效提取齿轮箱振动信号的故障特征,12组变工况实验平均识别准确率为98.38%,明显高于所提出的对比方法。     

You can get smaller: A lightweight self-activation convolution unit modified by transformer for fault diagnosis

The fault diagnosis methods based on convolutional neural network (CNN) have achieved many excellent results. However, owing to the deployment cost, numerous CNNs with large parameters are difficult to be directly applied to industrial practice. Therefore, this work aims to use lower parameters (order of magnitude is thousand) to complete the task of bearing fault diagnosis on the premise that the model has high-accuracy. To achieve this goal, a convolution unit modified by transformer was proposed, who is based upon the self-activation function, which makes the transformer and CNN organically integrated into a whole. Then, based on this unit, a series of novel lightweight diagnosis frameworks were proposed, named SANet. Finally, it was demonstrated that the proposed SANet can complete the high-accuracy diagnosis task with less than three thousand parameters and has strong robustness to noise (Average accuracy in various noise environments: 84.55%), and that SANet can achieve satisfactory results when there are few training samples (The number of samples of each category is 3 × 4), through four research cases. To sum up, based on this novel unit, we provide a series of lightweight frameworks with high-accuracy, strong robustness, and low sample demand, which is expected to promote the process of fault diagnosis technology from theoretical research to industrial practice.     

基于粒子群算法优化支持向量机汽车故障诊断研究

汽车故障检测和诊断技术一直是国内外研究热点问题。支持向量机用于汽车故障诊断时,其多分类组合决策对分类正确率及诊断时间有很大影响,为了有效提高汽车系统故障诊断的效率和精度,提出了一种基于粒子群算法优化层次支持向量机汽车故障诊断检测方法。针对分解支持向量机具有测试时间短、结构难以确定的特点,利用粒子群算法,依据最大间隔距离原则优化层次支持向量机模型,使每个节点的支持向量机具有最大分类间隔,减少了误差积累,从而优化了多级二叉树结构的SVM,实现故障的分级诊断。仿真实验结果表明,提出的算法在所有参比模型中精度最高,能高效地对汽车系统的故障进行检测与定位,具有较强的泛化能力,同时缩短了故障诊断时间。