下面是实现“Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks”所需的步骤: 步骤详解 1. 收集数据集 首先,你需要收集包含正常运行和故障运行的电机数据集。确保数据集具有充足的样本数量和多样性,以便训练和测试模型。 2. 数据预处理 对收集到的数据进行预处理是非常重要的,下面是一些可能的...
In this study, one-dimensional convolutional neural network (1D CNN) is used for automated damage detection using Lamb wave response of a thin aluminium plate. An attempt is made to interpret the 1D CNN model in terms of damage feature contributions using Local Interpretable Model-Agnostic ...
For the residual blocks, specify 64 filters for the 1-D convolutional layers with a filter size of 5 and a dropout factor of 0.005 for the spatial dropout layers. You can also build this network using the Deep Network Designer app. On the Deep Network Designer Start Page, in the ...
In this paper, we proposed autonomously generalized retrospective and patient-specific hybrid models based on two types of feature extractors, namely Convolutional Neural Networks along with long short-term memory. The model automatically generates customized features to better classify ictal, interictal, ...
将经验模态分解(Empirical Mode Decomposition, EMD)与一维卷积神经网络(One-Dimensional Convolutional Neural Network,1DCNN)相结合,提出一种基于声振信号的谐波减速器... 张熠鑫,徐洋,解国升 - 全国连接结构动力学学术研讨会会议 被引量: 0发表: 2023年 ...
与现有技术相比,本发明针对滚动轴承故障振动信号的非线性和非平稳特征,设计了一种自适应的一维卷积神经网络(1-DimensionalConvolutionalNeutralNetworks,1-DCNN)和长短时记忆网络(LongShort-TermMemory,LSTM)融合的轴承故障诊断方法:首先,将原始一维振动信号通过有重叠取样的方式分别输入1-DCNN和LSTM两个通道,然后通过维度...
alexnet模型在卷积神经网络(convolutional neural networks,cnn)的基础上,进行了许多拓展与优化。alexnet模型将relu函数作为激活函数,引入了局部响应归一化层(local response normalization,lrn),并在训练时采用神经元稀疏算法(dropout)随机忽略一部分神经元,从而避免过拟合,模型中使用了最大池化层(max pooling),提升了特征...
AlexNet模型在卷积神经网络(Convolutional Neural Networks,CNN)的基础上,进行了许多拓展与优化。AlexNet模型将ReLU函数作为激活函数,引入了局部响应归一化层(Local Response Normalization,LRN),并在训练时采用神经元稀疏算法(DropOut)随机忽略一部分神经元,从而避免过拟合,模型中使用了最大池化层(Max Pooling),提升了特征...
Convolutional neural networks are a type of DNN used to process data in multiple arrays, such as images. The key feature is the use of convolutional layers, which apply a set of filters to the input data, extracting relevant features and patterns. This allows image-specific features to be in...
This paper proposes a new method that combines an improved contact imaging technique, the images’ original color parameters, and a 1-D Convolutional Neural Network (CNN) specifically for tea leaves’ chlorophyll estimation. This method utilizes a smartphone and flashlight to capture tea leaf contact...