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。     

基于卷积神经网络多层特征提取的目标识别

目标识别一直是人工智能领域的热点问题. 为了提高目标识别的效率,提出了基于卷积神经网络多层特征提取的目标识别方法. 该方法将图像输入卷积神经网络进行训练,在网络的每个全连接层分别进行特征提取,将得到的特征依次输入到分类器,对输出结果进行比较. 选取经过修正线性单元relu函数激活的低层全连接层作为特征提取层,比选取高层全连接层特征提取的识别率高. 本文构建了办公用品数据集,实现了基于卷积神经网络多层特征提取的办公用品识别系统. 选择AlexNet卷积神经网络模型的relu6层作为特征选取层,选择最优训练图像数量和最优分类器构建系统,从而证明了该方法的可行性.     

药物-靶点亲和力预测的全局特征提取策略

为有效解决药物靶点亲和力预测中单模型提取特征种类受限问题,结合深度学习混合模型,提出一种深度并行全局特征提取策略.利用卷积神经网络(CNN)和特征存储融合层构建局部特征提取器,实现药物靶点序列局部特征的多层次提取、存储与压缩;利用卷积神经网络(CNN)和双向长短时记忆(BiLSTM)神经网络的串行混合模型构建上下文特征...     

基于改进胶囊网络的文本分类

针对卷积神经网络（CNN）中的池化操作会丢失部分特征信息和胶囊网络（CapsNet）分类精度不高的问题,提出了一种改进的CapsNet模型。首先,使用两层卷积层对特征信息进行局部特征提取;然后,使用CapsNet对文本的整体特征进行提取;最后,使用softmax分类器进行分类。在文本分类中,所提模型比CNN和CapsNet在分类精度上分别提高了3.42个百分点和2.14个百分点。实验结果表明,改进CapsNet模型更适用于文本分类。     

基于改进胶囊网络的文本分类

针对卷积神经网络（CNN）中的池化操作会丢失部分特征信息和胶囊网络（CapsNet）分类精度不高的问题，提出了一种改进的CapsNet模型。首先，使用两层卷积层对特征信息进行局部特征提取；然后，使用CapsNet对文本的整体特征进行提取；最后，使用softmax分类器进行分类。在文本分类中，所提模型比CNN和CapsNet在分类精度上分别提高了3.42个百分点和2.14个百分点。实验结果表明，改进CapsNet模型更适用于文本分类。     

基于一维卷积神经网络的实时道岔故障诊断

针对道岔故障诊断系统实时性要求高、特征提取严重依赖于先验知识的问题,提出了一种基于一维卷积神经网络（1D-CNN）的道岔实时故障诊断方法。以S700k转辙机的功率曲线为例,建立一维卷积神经网络的结构模型,该模型将特征提取与故障分类融合为一体,优化了网络参数,同时使用正则化Dropout提高模型的泛化能力,采用t-SNE可视化方法,来反映模型提取特征的有效性。仿真实验表明：卷积层和池化层对原始时域信号的自适应特征提取,能较好地捕捉信号空间维度信息,降低模型的计算量,提高模型的抗噪性能,实现了端到端的实时故障诊断,并有效地提高道岔故障实时诊断的准确率。     

基于AC-CNN模型的过程故障识别

针对复杂工业过程中故障变量特征提取效率低,分类数量较少且故障识别率较低等问题,提出基于非对称卷积核(asymmetric convolutions)的卷积神经网络(CNN)的工业过程故障识别模型。采取故障变量重构对故障数据进行预处理;引入非对称卷积核模型对重构后的输入故障变量进行特征提取,提高特征提取的效率;根据CNN模型改进得到具有AC架构的AC-CNN模型,识别TE(田纳西-伊斯曼)过程故障的在线测试集样本,实验结果表明,所提方法对TE过程故障数据集的识别效果明显,验证了模型的有效性和优异性。     

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1D-CNN-LSTM注意力网络的抽油机井故障诊断

针对传统基于示功图的抽油机井故障诊断方法存在特征提取复杂、模型参数量大、诊断效率低的问题，提出一种基于1D-CNN-LSTM注意力网络的故障诊断方法。将示功图转化为载荷位移序列作为网络输入，使用一维卷积神经网络（1D-CNN）在提取序列局部特征的同时减小序列长度；考虑到序列的时序特性，进一步使用长短时记忆网络（LSTM）提取序列的时序特征；为突出关键特征影响，引入Attention机制，对故障类型相关的时序特征赋予更高的注意力权重；最后将加权特征输入全连接层，利用Softmax分类器实现故障诊断。实验结果表明，所提方法的平均准确率、精确率、召回率和F1值分别达到99.13%、99.35%、99.17%和99.25%，模型大小仅为98 kB。相比基于特征工程的方法具有更高的诊断精度和泛化能力，相比基于二维卷积神经网络（2D-CNN）模型的诊断方法，显著减少了模型参数量和训练时间，提高了故障诊断效率。     

细菌觅食算法优化卷积神经网络的滚动轴承故障诊断

针对传统深度学习故障诊断方法在滚动轴承中诊断效果不理想的问题，提出一种细菌觅食优化算法(BFO)优化卷积神经网络(CNN)学习率使诊断效果提升的模型。在模型逐次迭代过程中，将CNN中的学习率参数带入BFO中，生成一个自适应的学习率，用于更新CNN的权重和偏置，使模型故障诊断效果达到最佳。通过实验证明基于细菌觅食算法优化的卷积神经网络训练的模型在分类精度上优于CNN训练的模型，并与CNN多种学习率对比，可将故障诊断准确率提升至97.25%,并提高了全局的收敛能力。     

基于LMD-PE与神经网络的刀具故障诊断方法

针对刀具故障诊断信号信噪比低、诊断结果不准确等问题,采用局域均值分解(LMD)结合排列熵(PE)来处理采集到的刀具加工时的振动信号,然后将提取到的特征向量输入到训练好的长短期记忆神经网络(LSTM)中得到诊断结果,为了提高LSTM的诊断效率,结合卷积神经网络(CNN)对LSTM进行了改造;试验表明,文章提出的方法诊断准确率比BP神经网络提高了将近12%,改进LSTM网络比传统LSTM的诊断时间缩短了50%。     

AKSNet: A novel convolutional neural network with adaptive kernel width and sparse regularization for machinery fault diagnosis

Convolutional kernels have significant affections on feature learning of convolutional neural network (CNN). However, it is still a challenging problem to determine appropriate kernel width. Moreover, some features learned by convolutional layers are still redundant and noisy. Thus, adaptive selection of kernel width and feature selection of feature maps are key techniques to improve feature learning performance of CNNs. In this paper, a new deep neural network (DNN) model, adaptive kernel sparse network (AKSNet) is proposed to extract multi-scale fault features from one-dimensional (1-D) vibration signals. Firstly, an adaptive kernel selection method is developed, where multiple branches with different kernels are used to extract multi-scale features from vibration signals. Channel-wise attention is developed to fuse features generated by these kernels to obtain different informative scales. Secondly, a spatial attention is used for dynamic receptive field to focus on salient region of feature maps. Thirdly, a sparse regularization layer is embedded in the deep network to further filter noise and highlight impaction of the feature maps. Finally, two cases are adopted to verify effectiveness of AKSNet-based feature learning for bearing fault diagnosis. Experimental results show that AKSNet can effectively extract features from multi-channel vibration signals and then improves fault diagnosis performance of the classifier significantly. AKSNet shows better recognition performance in comparison with that of shallow neural networks and other typical DNNs.     

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。     

An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump

Hydraulic piston pump is a vital component of hydraulic transmission system and plays a critical role in some modern industrials. On account of the deficiencies of traditional fault diagnosis in preprocessing of original data and feature extraction, the intelligent methods based on deep learning accomplish the automatic learning of fault information by integrating feature extraction and classification. As a popular deep learning model, convolutional neural network (CNN) has been demonstrated to be potent and effective in image classification. In this research, an improved intelligent method based on CNN with adapting learning rate is constructed for fault diagnosis of a hydraulic piston pump. Firstly, three raw signals are converted into two dimensional time–frequency images by continuous wavelet transform, including vibration signal, pressure signal and sound signal. Secondly, an improved deep CNN model is built with an adaptive learning rate strategy for identifying the different fault types. Moreover, t-distributed stochastic neighbor embedding is employed to visualize the distribution of features learned by the main layers of CNN model. Confusion matrix is used to analyze the classification accuracy of each fault type. Compared with the CNN model without adapting learning rate, the improved model achieves a higher accuracy based on the selected three kinds of signals. Experiments indicate that the improved CNN model can effectively and accurately identify various faults for a hydraulic piston pump.     

基于改进卷积神经网络的电力通信网故障诊断研究

针对当前电力通讯网络故障诊断方法及时性差、准确率低和自我学习能力差等缺陷,提出基于改进卷积神经网络的电力通信网故障诊断方法,结合ReLU和Softplus两个激活函数的特点,对卷积神经网络原有激活函数进行改进,使其同时具备光滑性与稀疏性;采用ReLU函数作为作为卷积层与池化层的激活函数,改进激活函数作为全连接层激活函数的结构模型,基于小波神经网络模型对告警信息进行加权操作,得到不同告警类型和信息影响故障诊断和判定的权重,进一步提升故障诊断的准确率;最后通过仿真试验可以看出,改进卷积神经网络相较贝叶斯分类算法与卷积神经网络具有较高的准确率和稳定性,故障诊断准确率达到99.1%,准确率标准差0.915%,为今后电力通讯网智能化故障诊断研究提供一定的参考。     

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...     

Intelligent fault recognition framework by using deep reinforcement learning with one dimension convolution and improved actor-critic algorithm

The quality of fault recognition part is one of the key factors affecting the efficiency of intelligent manufacturing. Many excellent achievements in deep learning (DL) have been realized recently as methods of fault recognition. However, DL models have inherent shortcomings. In particular, the phenomenon of over-fitting or degradation suggests that such an intelligent algorithm cannot fully use its feature perception ability. Researchers have mainly adapted the network architecture for fault diagnosis, but the above limitations are not taken into account. In this study, we propose a novel deep reinforcement learning method that combines the perception of DL with the decision-making ability of reinforcement learning. This method enhances the classification accuracy of the DL module to autonomously learn much more knowledge hidden in raw data. The proposed method based on the convolutional neural network (CNN) also adopts an improved actor-critic algorithm for fault recognition. The important parts in standard actor-critic algorithm, such as environment, neural network, reward, and loss functions, have been fully considered in improved actor-critic algorithm. Additionally, to fully distinguish compound faults under heavy background noise, multi-channel signals are first stacked synchronously and then input into the model in the end-to-end training mode. The diagnostic results on the compound fault of the bearing and tool in the machine tool experimental system show that compared with other methods, the proposed network structure has more accurate results. These findings demonstrate that under the guidance of the improved actor-critic algorithm and processing method for multi-channel data, the proposed method thus has stronger exploration performance.     

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。