Results 71 to 80 of about 12,210 (170)
, 2022 针对在强烈背景噪声影响下的滚动轴承故障特征提取困难，提出了一种基于变分模态分解与卷积神经网络融合的滚动轴承故障诊断方法。将原始振动信号分解为多个模态分量，结合皮尔逊相关系数作为自动分解终止阈值和最优模态分量选取指标；针对轴承故障特征构建卷积神经网络，将最优模态分量作为输入以提取、分类故障类型。试验结果表明，所提方法能够精确诊断滚动轴承故障 ...
刘春阳 +5 more
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Acoustic signal enhancement method for belt conveyor idler bearings [PDF]
To address the issues in existing acoustic signal enhancement methods for belt conveyor idler bearings, such as excessive noise reduction leading to signal distortion, poor adaptability, and ineffective extraction of complex sound field characteristics ...
MA Bo, WU Peng, YOU Qinghua
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Zhendong Ceshi yu Zhenduan针对传统辛几何模态分解（symplectic geometry mode decomposition， 简称SGMD）方法因嵌入维数选择依靠经验公式，导致出现信号模态混叠和过度分解的问题，提出了一种改进的辛几何模态分解（improved symplectic geometry mode decomposition， 简称ISGMD）方法。首先，通过计算原始信号的功率谱密度得到最大主峰的频率并设定嵌入维数区间，根据峭度准则筛选分解后的辛几何分量（symplectic geometry component，
doaj +1 more source
High-Precision Fault Diagnosis Method for Energy Storage Inverter Signals [PDF]
Owing to the global trends of energy transition and carbon neutrality, ensuring the stability of energy storage inverters as key components is crucial.
Yu WANG, Qi QI, Chun WANG, Cai XU
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Gaoya dianqi针对非平稳变压器油中溶解气体序列既有长期趋势又有短期细微波动的复杂特性，文中将黄金正弦算法（GSA）优化的麻雀搜索算法（SSA）与变分模态分解（VMD）组合构成GSSA-VMD模型；对原始变压器油中溶解气体序列使用GSSA-VMD分解，最终得到一组平稳的模态分量；其次，为了精准预测变压器气体序列长期趋势和短期波动，文中将时序卷积网络（TCN）与长短期记忆网络（LSTM）组合起来，并与GSSA-VMD组合构成变压器油中溶解气体含量组合预测模型；最后，文中选用变压器油中溶解气体CO2进行实验验证，与VMD ...
代浩 +6 more
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Unsupervised Specific Emitter Identification Method Based on Directed Graph Connectivity [PDF]
Specific emitter identification （SEI） refers to the technique of distinguishing emitters by utilizing unique and subtle features in received electromagnetic signals.
DING Guoru +4 more
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Research on Fault Diagnosis Method of Photovoltaic Arrays Based on Improved Grey Wolf Algorithm Optimized Extreme Learning Machine [PDF]
ObjectivesPhotovoltaic arrays operating under complex outdoor conditions encounter various fault types with varying degrees of severity. To accurately assess the working status of photovoltaic arrays, a fault diagnosis method based on an improved grey ...
HAN Maolin +4 more
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, 2020 针对齿轮箱的滚动轴承故障信号因噪声干扰，难以进行有效提取的问题，提出了基于变分模态分解与快速谱峭图相结合的轴承故障特征提取方法。首先，利用变分模态分解（Variational Mode Decomposition，VMD）将振动信号分解成若干个本征模态分量（Intrinsic Mode Function，IMF），通过相关峭度计算选取故障信息最突出的分量信号；然后，利用快速谱峭图自适应地确定带通滤波；最后，对滤波后的信号进行平方包络谱分析，提取出故障信息。通过公开数据分析和齿轮箱轴承故障实验 ...
康建设, 池阔, 迭旭鹏
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Efficient Seismic Denoising Transformer with Gradient Prediction and Parameter-Free Attention [PDF]
Suppression of random noise can effectively improve the signal-to-noise ratio (SNR) of seismic data. In recent years, convolutional neural network (CNN)-based deep learning methods have shown significant performance in seismic data denoising.
GAO Lei, QIAO Haowei, LIANG Dongsheng, MIN Fan, YANG Mei
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Gaoya dianqiGIS设备振动信号的变化可以反映设备内部的机械状态。为了提高GIS设备振动信号特性的预测精度，文中提出了一种基于分解—预测—重构的组合组测模型。首先，基于GIS历史振动信号，通过傅里叶变换在频域提取振动特征参数；其次，为了尽可能消除振动特征参数序列非平稳特性带来的影响，将归一化后的序列使用经过粒子群算法（PSO）优化后的变分模态分解（VMD）对振动特征参数序列进行分解；最后，将分解得到的一组平稳化模态分量使用时间卷积网络（TCN）进行预测。实验结果表明，文中所提基于PSO-VMD ...
王谦 +6 more
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